Archives of Academic Emergency Medicine

Introduction: Acute liver complications of pregnancy (ALCP) remain major contributors to maternal morbidity. This study aimed to characterize cardio-hepatic crosstalk and identify independent predictors of hepatic failure and major cardiovascular events in patients with ALCP.

Methods: This retrospective cohort study was conducted to investigate cardio-hepatic crosstalk, endothelial dysfunction, and cardiovascular risk stratification among pregnant women with ALCP (4 diagnostic categories: HELLP syndrome, pre-eclampsia with hepatic involvement, acute fatty liver of pregnancy, and intrahepatic cholestasis of pregnancy). Independent predictors of cardiac and hepatic outcomes following ALCP, among endothelial, cardiac, inflammatory, coagulation, and renal biomarkers, were detected using correlation, multivariable regression, and receiver operating characteristic (ROC) analyses.

Results: 400 patients with ALCP were studied. All 22 studied biomarkers differed significantly across diagnostic categories. The ratio of soluble fms-like tyrosine kinase-1 to placental growth factor (sFlt-1/PlGF ratio) correlated most strongly with N-terminal pro-B-type natriuretic peptide (NT-proBNP) (r = 0.57, p < 0.001) and independently predicted major cardiovascular events (adjusted odds ratio (OR) = 1.38, 95% confidence interval (CI) = 1.03–1.85). C-reactive protein and creatinine independently predicted hepatic failure, with C-reactive protein showing the highest discriminative accuracy (area under the ROC curve of 0.79 (95% CI: 0.69–0.89).

Conclusions: Hepatic and cardiovascular derangement in ALCP constitute an interconnected, biomarker-detectable syndrome, supporting integrated biomarker-guided risk stratification for emergency management and delivery-timing decisions.

Introduction: Distractions and interruptions may interfere with concentration, working memory, and timely decision-making. This study aimed to analyze the visual attention patterns of medical interns during the management of a multiple-trauma patient in a realistic virtual reality (VR) emergency scenario using eye-tracking technology integrated into VR simulation.

Methods: This cross-sectional simulation-based study was conducted among 15 medical interns at Tehran University of Medical Sciences. Participants were exposed to a 242-second high-fidelity VR trauma scenario representing a crowded ED with multiple environmental distractions and interruptions. Eye-tracking data were collected using the HTC Vive Pro Eye headset. The scenario was divided into four predefined temporal intervals. Areas of interest (AOIs) were defined and classified as relevant (main patient, key treatment team members, and essential equipment) or distractors (nonessential environmental and emotional stimuli). Main outcome measures included cumulative gaze time on relevant and distractor AOIs, gaze time outside defined AOIs, and the Relevant/Distractor ratio.

Results: A total of 253,075 gaze samples were obtained, with a mean of 16,871.7 ± 2,097.0 samples per participant. The mean duration of valid eye-tracking data was 238.5 ± 12.3 seconds per participant, corresponding to approximately 98.6% of the total scenario time. Across all intervals, gaze was predominantly directed toward relevant AOIs, and this pattern became progressively stronger over time. The Relevant/Distractor ratio increased from 3.41 ± 1.58 in the initial interval to 27.60 ± 26.12 in the final interval, while attention to distractor AOIs decreased from 19.2% to 4.7%. Spatially, the head and face of the main injured patient, the emergency medicine resident, and the patient monitor consistently attracted the greatest attention.

Conclusion: Medical interns progressively adopted a more structured and goal-directed visual attention strategy, increasingly prioritizing vital clinical information over distracting stimuli. VR-based eye tracking appears to be a feasible and informative tool for assessing attentional behavior and may support targeted educational interventions in trauma training.

Introduction: Emergency department length of stay (ED-LOS) is a key indicator of crowding and care quality. This study aimed to identify patient-level, temporal, and real-time operational predictors of prolonged ED-LOS.

Methods: We conducted a retrospective cohort study using routinely collected data from all ED visits at Foch Hospital, France, between January and November 2025. Prolonged ED-LOS was defined as ED-LOS >8 hours. Data were split chronologically into a training period from January to September and a held-out test period from October to November. Three logistic regression models were evaluated: Model 1 included patient-level and temporal variables; Model 2 additionally included dynamic congestion indicators; and Model 3 further included early process delays.

Results: Among 41,818 ED visits, 41,431 were included in the final analytic sample. Overall, 8,119 (19.6%) visits had ED-LOS >8 hours. In the test set, Model 1 showed good discrimination (area under the receiver operating characteristic curve (AUC): 0.752, 95% confidence interval (CI): 0.740 – 0.766), which improved modestly after adding dynamic congestion variables in Model 2 (AUC: 0.761, 95% CI: 0.749 – 0.775). Model 3 achieved the best performance (AUC: 0.804 (95% CI: 0.793 – 0.815); Brier score: 0.127 (95% CI: 0.122 – 0.132)). Older age, triage acuity level 3 (classification infirmière des malades aux urgences: CIMU ), weekend arrival, dynamic congestion at arrival, and early process delays were the main predictors of prolonged ED-LOS.

Conclusion: Based on the findings, older age, intermediate triage acuity, weekend arrival, dynamic congestion at arrival, and early process delays were the independent predictors of prolonged ED stay. The addition of dynamic congestion variables improved prediction beyond patient-level and temporal characteristics, while the strongest performance was achieved after incorporating early delays to triage and physician assessment.

Introduction: Bystander-initiated Cardiopulmonary Resuscitation (CPR) strongly influences the Out-of-hospital cardiac arrest (OHCA) survival outcomes; but substantial disparities persist in knowledge levels and willingness to perform CPR. This study aimed to evaluate the CPR knowledge and willingness of bystanders to perform standard versus hands-only CPR among Jordanian adults.

Methods: A nationwide cross-sectional study was conducted using a validated questionnaire developed in accordance with American Heart Association (AHA) guidelines. The questionnaire assessed demographic characteristics, prior CPR training, knowledge (10 items), and willingness (attitude) of bystanders to perform CPR (12 items). Descriptive statistics were used to present the findings, while chi-square and McNemar tests were used to compare groups.

Results: A total of 1,242 participants with a mean age of 27.38 ± 9.3 (range: 18-70) years, completed the survey (65.6% female). The mean knowledge score was 4.2 ± 2.3 (range: 0–10), with trained individuals scoring significantly higher than untrained participants (6.1 vs. 3.2, p<0.001). Trained participants exhibited greater willingness to perform CPR across all scenarios (p<0.001), whereas untrained individuals showed higher willingness to provide hands-only CPR, particularly for strangers and neighbors (p<0.01). Males were significantly more inclined to perform CPR across all scenarios (p<0.01), whereas females were more willing to provide hands-only CPR in certain scenarios (p<0.01). Nevertheless, most of the differences became statistically insignificant after restricting the analysis to respondents who witnessed a cardiac arrest event.

Conclusion: Trained individuals showed higher levels of willingness, yet untrained participants demonstrated unexpected willingness to perform hands-only CPR, while female participants were consistently less willing to perform standard CPR. However, sensitivity analysis restricted to respondents who had witnessed a cardiac arrest showed that real-life exposure substantially reduces hesitation and narrows group disparities.

Introduction: Stroke is a leading global cause of disability, with large-vessel occlusion (LVO) representing a significant proportion of ischemic strokes and requiring timely endovascular thrombectomy (EVT) for optimal outcomes. This study aimed to validate and compare RAMA-LVO with other prehospital LVO prediction scales.

Methods: This retrospective, single-center diagnostic accuracy study included adult patients who were triaged as suspected acute stroke in the emergency department (ED) of a university hospital. Four prehospital LVO prediction scales including RAMA-LVO, Rapid Arterial oCclusion Evaluation (RACE), Field Assessment Stroke Triage for Emergency Destination (FAST-ED), and Los Angeles Motor Scale (LAMS) were calculated from documented neurological assessments and compared against confirmed vascular imaging findings. Diagnostic performance was evaluated using the area under the receiver operating characteristic curve (AUROC), with comparative analysis by DeLong’s test and calibration plots to assess model fit.

Results: Of the 1,463 patients with suspected acute stroke, 853 patients met the inclusion criteria, with 124 (14.54%) confirmed to have large-vessel occlusion (LVO), most commonly involving the M1 segment of the middle cerebral artery. Patients with LVO were older (p < 0.001), had higher National Institutes of Health Stroke Scale (NIHSS) scores (p < 0.001), and were more likely to receive intravenous thrombolysis or endovascular thrombectomy (p < 0.001) compared with non-LVO patients.

Among the four prehospital LVO scales, FAST-ED showed the highest discriminative performance (AUROC = 0.873), closely followed by RAMA-LVO (AUROC = 0.858), while RAMA-LVO demonstrated the highest sensitivity but slightly lower specificity relative to other scales.

Conclusion: The RAMA-LVO score demonstrated strong accuracy for identifying acute LVO stroke, comparable to FAST-ED, and may serve as a practical prehospital triage tool for directing patients to thrombectomy-capable centers, especially in regions with transfer delays.

Introduction: On-scene interval (OSI) is a modifiable component of prehospital care, but the interpretation of prolonged OSI is complex because longer scene time may reflect either avoidable delay or necessary life-saving care. This study aimed to evaluate the association between OSI and 24-hour mortality among Code Red emergency medical services (EMS) patients and to identify an operational OSI range that may warrant quality-improvement review. Methods: We conducted a retrospective cohort study of all eligible Code Red EMS activations dispatched with advanced life support teams in Saraburi Province, Thailand, from May 2017 to July 2019. EMS dispatch records, ambulance run sheets, and hospital electronic medical records were used. OSI was defined as the interval from EMS arrival at the scene to departure from the scene. OSI was categorized in 5-minute intervals for operational interpretability and was also modeled continuously using restricted cubic splines. The primary outcome was all-cause mortality within 24 hours of EMS activation. Multivariable logistic regression, adjusted predicted probabilities, and restricted mean survival time were used to evaluate associations. Results: Of 2,500 Code Red EMS activations screened, 2,321 patients were included. The 24-hour mortality rate was 5.1% (118/2,321). Non-survivors had a longer median OSI than survivors (12 [interquartile range (IQR): 9-16] vs. 9 [IQR: 6-12] minutes; p < 0.001). After adjustment, OSI of 21-25 minutes was associated with higher odds of 24-hour mortality compared with OSI of 5 minutes or less (adjusted OR: 5.61, 95% CI: 1.58-19.97; p < 0.01). Restricted cubic spline analysis suggested a non-linear increase in mortality risk with longer OSI, although estimates at longer intervals were imprecise because of sparse data. Patients with OSI of 20 minutes or longer had 321.7 fewer expected survival minutes within the 24-hour horizon than those with OSI shorter than 20 minutes (95% CI: -530.1 to -113.2; p = 0.002). Low Glasgow Coma Scale score (p <0.001) and prehospital life-saving interventions (p <0.001) were the strongest predictors of death. Conclusions: Prolonged OSI was associated with 24-hour mortality, with the clearest adjusted signal observed at 21-25 minutes. However, this finding should be interpreted cautiously because patient severity and the need for life-saving interventions were dominant predictors of mortality, and the optimal OSI threshold may differ by clinical condition. OSI should be used as an operational quality-improvement benchmark rather than a standalone prognostic marker or universal cutoff.

Introduction: Traumatic out-of-hospital cardiac arrest (TOHCA) in patients with traumatic brain injury (TBI) is associated with exceptionally poor survival. However, prehospital prognostic factors influencing early survival in this high-risk population remain poorly defined. This study aimed to identify prehospital factors associated with survival to hospital admission among TOHCA patients with TBI in Thailand.

Methods: We conducted an 11-year nationwide retrospective cohort study using the Information Technology of Emergency Medicine System (ITEMS), Thailand’s national EMS registry (2012–2022). TOHCA patients with documented TBI who were transported to the emergency department (ED) by emergency medical services (EMS) were included. The primary outcome was survival to hospital admission. Multivariable logistic regression was used to identify independent prehospital factors associated with survival.

Results: Of 46,760 TOHCA cases, 22,821 involved TBI, and 16,885 met inclusion criteria with recorded ED outcomes. Overall, 2,872 patients (17.0%) survived to hospital admission. Younger age, longer on scene time, and shorter hospital-to-scene distance were independently associated with improved odds of survival. Several prehospital interventions showed significant benefit, including external bleeding control (adjusted odds ratio (aOR) 1.38, 95% confidence interval (CI): 1.20–1.58), endotracheal intubation (aOR 2.09, 95% CI: 1.69–2.57), intravenous fluid administration (aOR 1.56, 95% CI: 1.24–1.96), and defibrillation (aOR 2.05, 95% CI: 1.66–2.53). In contrast, on-scene time <10 minutes (aOR 0.65, 95% CI: 0.53–0.79) and bone and joint injuries, including closed fractures, open fractures, and dislocations, were associated with reduced survival.

Conclusion: Despite the generally poor prognosis of TOHCA patients, particularly those with TBI, our findings demonstrate that adequate on-scene time and the prompt delivery of critical prehospital interventions, including external bleeding control, intubation, hydration, and defibrillation, were independently associated with improved survival to hospital admission.

Introduction: Traumatic spinal cord injury (TSCI) remains a significant cause of long-term disability, with mortality and pressure ulcer (PU) development being key determinants of patient outcomes. This study aimed to develop and interpret machine learning (ML) models for predicting in-hospital mortality and PU formation in TSCI patients using data from the National Spinal Cord Injury Registry of Iran (NSCIR-IR). Methods: Patients with TSCI admitted between 2015 and 2023 were included in the analysis. Data preprocessing included iterative imputation, feature selection with cross-validation, and class balancing using SMOTE. Nine ML algorithms were trained and validated on an 80:20 split dataset. Model performance was assessed using the area under the receiver operating characteristic curve (AUC) and other standard metrics. Feature importance and interpretability were evaluated using permutation importance and Shapley Additive Explanations (SHAP) analysis. Results: A total of 499 patients were included. The LightGBM classifier achieved the highest performance in mortality prediction (AUC = 0.85; 95% CI: 0.73-0.94), followed by KNN and Naive Bayes. For PU prediction, the Random Forest model performed best (AUC = 0.83; 95% CI: 0.72-0.93), outperforming XGBoost and LightGBM. Ventilator use, ASIA grade, and sensory and motor scores were the strongest predictors of mortality, while first aid given, ventilator use, and the number of injured vertebrae were the strongest predictors of PU risk. SHAP analysis confirmed these findings. Conclusion: ML algorithms can accurately identify TSCI patients at high risk of mortality and PU development using routinely collected clinical data. Integrating such interpretable ML tools into early triage systems could enable timely preventive interventions and improve patient outcomes.

Introduction: Scorpion sting (SS) resulting in envenomation is a life-threatening emergency and causes serious health problems in tropical and subtropical regions. This study aimed to present the epidemiologic and clinical features of scorpion sting in the mediterranean region of Turkey.

Methods: In this cross sectional study the epidemiologic, clinical characteristics, and outcomes of cases who presented to the Emergency Department (ED) of Antalya Training and Research Hospital with scorpion stings between January 2017 and December 2020 were analyzed using SPSS version 23.

Results: 843 cases with the mean age of 40.49±21.26 years were evaluated (19.8% <18 years and 50.5% female). Allmost all patients (98%), had been stung by a yellow scorpion. The majority of patients were in grade 1 and grade 2 severiry (40.1%, n=338; 47.9%, n=404, respectively) according to the clinical grading. One patient died, 4.4% (n=36) were admitted to the intensive care unit and 89.4% of the patients were discharged from the ED. While cases of scorpion stings in children were prevalent during the winter months, cases affecting adults and the elderly were more common during the summer and autumn.

Conclusion:  The majority of scorpion stings in Antalya and its environs were with yellow scorpion, in urban area, among adults,  in distal parts of the extremities, and with mild clinical severity. There was a significant correlation between age group and seasona, age group and laboratory findings, and clinical severity and laboratory findings.

Introduction: Bibliometrics tends to analyze the scientific research items published on a specific field or topic. This study aimed to analyze Iranian researchers’ production in the field of emergency medicine (EM).

Methods: This bibliometric visualization study was conducted using a descriptive-analytical approach and based on data from the Scopus database. All scientific output of Iranian researchers in the field of EM by the end of 2024 was collected and analyzed using VOSviewer 1.6.20 software.

Results: Iran's scientific output in the field of EM has grown significantly. The Archives of Academic Emergency Medicine is the top journal and Tehran University of Medical Sciences is the top institution publishing the scientific productions of Iranian researchers in the field. The United States, United Kingdom, Canada, and Australia are Iran's largest scientific collaborating partners. Iran is the second-ranked leading country in the Middle East, publishing 20.38% of the Middle Eastern scientific publications in the field. Out of 17 countries in the Middle East, only two countries, Turkey and Iran, are in the top 20 countries in the world. The co-occurrence map of keywords showed the five thematic clusters, including risk factors and clinical outcomes, clinical symptoms and laboratory indicators, emergency and psychiatric services, medical diagnostic and imaging tools, and vital signs and clinical monitoring.

Conclusion: As the second-ranked country in the Middle East and the 16th-ranked in the world, Iran has a prominent position and a high capacity for scientific development and research diplomacy in EM. The trend of scientific productions of EM in Iran is also growing. Promoting this status and maintaining this trend requires special attention from officials and policy makers to research, expanding international cooperation, increasing the quality of research and focusing on interdisciplinary and problem-oriented research.

Data registry systems are vital tools for systematically collecting, storing, and analyzing disease-specific data within defined patient populations. This protocol presents a national registry system designed to prospectively collect data on patients undergoing reconstructive urology surgeries in Iran.

A web-based registry involving multiple centers across Iran with official collaboration agreements will collect demographic, clinical, surgical, and follow-up data. Patients meeting inclusion criteria are enrolled following informed consent. Data confidentiality and integrity are maintained using a secure software hosted at the Men’s Health and Reproductive Health Research Center in Tehran. Standardized clinical questionnaires (IIEF) and checklists capture patient outcomes prospectively up to 27 months post-surgery.

The registry will provide epidemiological insights, monitor treatment outcomes, complications, and clinical improvements, extraction of guidelines, and policy brief, and support collaborative research nationally and internationally. This registration system was designed to achieve evidence-based data with the coherence of patient data for policy-making and therapeutic interventions.

 Patients Reported Outcome Measure for Urethral Stricture Surgery.  International Consultation of Incontinence Questionnaire- lower urinary tract symptoms Quality of Life.

 

 

Introduction: Severe traumatic brain injury (TBI) is a leading cause of death and long-term disability worldwide. Therapeutic hypothermia may lower intracranial pressure (ICP) and reduce metabolic demand. This study aims to compare mild therapeutic hypothermia with controlled normothermia in adult TBI patients undergoing Barbiturate Coma Therapy (BCT), and to explore artificial intelligence (AI)-assisted prognostic models. Methods: This single-center, pilot, parallel-group randomized trial evaluates the feasibility and safety of adding mild hypothermia to BCT and explores early signals of benefit or harm. The primary outcome, six-month Glasgow Outcome Scale–Extended (GOSE), is reported descriptively with exact 95% confidence intervals. Secondary outcomes include Cerebral Performance Category, mortality, duration of mechanical ventilation and vasopressor use, intensive care unit (ICU)/hospital length of stay, complications, and physiological/laboratory trends. An exploratory AI-assisted substudy (using prospectively collected clinical, radiological, physiological, and laboratory data) applies LASSO regression to admission predictors of six-month unfavorable outcome and mortality.

Introduction: The online body camera system (OBCS) integrates telemedicine technology with real-time monitoring of on-scene operations, direct online medical direction, and coordinated patient care. This study aimed to evaluate its effectiveness compared with the traditional telephone-based medical oversight system.

Methods: This retrospective cohort study evaluated the effectiveness of OBCS in prehospital emergency care of Ramathibodi Hospital by comparing outcomes (survival rate, length of stay) between the traditional telephone-based (2014–2021) and the OBCS periods (2022–2024) in emergency medical operation unit.

Results: This 11-year retrospective cohort study analyzed 5,721 prehospital emergency care cases (3,398 cases managed under the traditional telephone-based system and 2,323 cases managed under the OBCS). The findings indicate that the implementation of the OBCS significantly improved overall survival to emergency department (ED) arrival (adjusted odds ratio (aOR) = 8.32, 95% CI: 1.47–47.14, P = 0.017), particularly among out-of-hospital cardiac arrest (OHCA) patients (aOR = 7.99, 95% CI: 1.28–50.03, P = 0.026). However, the new system did not result in significant improvements in survival to hospital admission or survival to hospital discharge. Furthermore, the length of stay (LOS) in the ED, intensive care unit (ICU), and overall hospital setting remained unchanged.

Conclusions: The OBCS significantly enhanced survival to ED arrival, particularly among OHCA patients. However, the LOS in the ED, ICU, and overall hospital setting remained unchanged.

Introduction: Nanocarrier-mediated delivery represents a promising strategy for enhancing the therapeutic efficacy of pharmacological agents. This study investigated whether encapsulating the endogenous peptide apelin-13 in niosomes (Nano-Apelin13) could improve its protective effects against ischemic stroke injury in rats.

Methods. Transient focal cerebral ischemia was induced by 60-minute middle cerebral artery occlusion (MCAO) followed by 24-hour reperfusion. Sixty rats were randomly allocated into five experimental groups: Sham, CI/R (ischemia/reperfusion), CI/R + empty niosomes, CI/R + free Apelin13, and CI/R + Nano-Apelin13. Free Apelin13 and Nano-Apelin13 (40 µg/kg) were administered intravenously 1 hour after reperfusion onset. After 24 hours, cerebral infarction size, neurological deficit scores, brain water content, and neuronal damage were evaluated. Protein expression levels of TLR4, MyD88, NF-κB, TNF-α, and IL-1β were analyzed by western blotting.

Results. Both free Apelin13 and Nano-Apelin13 significantly reduced cerebral infarct volume, improved neurological scores, decreased neuronal loss, and downregulated MyD88 and NF-κB expression. However, Nano-Apelin13 demonstrated superior efficacy, normalizing several parameters to sham-operated levels. Notably, Nano-Apelin13 significantly suppressed TLR4 expression and attenuated cerebral edema and pro-inflammatory cytokine production, effects that were either significantly enhanced or absent in the free Apelin13-treated group.

 Conclusion: Delivery of Apelin13 in niosomes could be a good option to reduce CI/R damage by inhibiting the activity of TLR4/MyD88/NF-kB axis compared to free Apelin13.

Introduction: Although anticoagulation is a well-established risk factor for intracranial hemorrhage (ICH) following trauma, its role in determining the need for neuroimaging in patients with nontraumatic headache is less clear. This study evaluated whether anticoagulation alone justifies brain computed tomography (CT) scan imaging in non-traumatic headache.

Methods: We conducted a retrospective cohort study at a tertiary care center in Riyadh, Saudi Arabia, including adults presenting with nontraumatic headache between January 2020 and 2023. Patients who underwent brain CT scan were assessed for hemorrhage and potential associated factors of ICH were explored.

Results: Among 909 patients who presented with non-traumatic headache, 162 (17.82%) cases were on anticoagulation therapy, and 20 (2.2%) had ICH on brain CT scan. Anticoagulation use alone was not significantly associated with the risk of ICH following nontraumatic headache (p = 0.068). A history of prior ICH or ischemic stroke emerged as a predictor of ICH, in the overall cohort and among anticoagulated patients (p < 0.001 for all comparisons). No statistically significant associations were observed with antiplatelet use, anticoagulant type, combined anticoagulant–antiplatelet therapy, or neurological deficits.

Conclusion: It seems that, in patients presenting with nontraumatic headache, anticoagulation use alone should not dictate the decision to perform brain CT scan. A history of prior ICH or ischemic stroke is a more reliable predictor of ICH risk and should be prioritized in imaging decisions. These findings support a more targeted imaging strategy to minimize unnecessary scans and improve emergency department resource utilization.

Introduction: Prehospital care plays a crucial role in early recognition and timely management of foreign body airway obstruction (FBAO). This study aimed to identify factors associated with dispatcher performance and prehospital outcomes in the telephone-assisted management of FBAO.

Methods: This cross-sectional study was conducted through a review of recorded emergency telephone calls related to FBAO. Data were collected from the prehospital emergency medical services center over a one-year period, from March 20, 2024 to March 20, 2025. Demographic characteristics of dispatcher were obtained through self-reported questionnaires. Recorded calls were assessed using validated 13-item researcher-developed checklist based on the standardized telephone assistance algorithm for FBAO, categorized across three age groups (score range 0-1). Patient demographic characteristics, clinical characteristics, and prehospital outcomes, including recovery without dispatch of an operational team and dispatch of an operational team, were extracted from electronic medical records. Data were entered and analyzed using SPSS version 16. A p-value < 0.05 was considered statistically significant

Results: A total of 262 telephone assistance FBAO cases were included in the final analysis, of which 45.8% involved infants. The overall mean dispatcher performance score was 0.87 ± 0.10. Multivariable logistic regression analysis demonstrated that patient’s age (adjusted odds ratio (AOR) = 1.60, 95% confidence interval (CI): 1.13–2.26, p = 0.008) and dispatcher’s clinical experience (AOR = 1.44, 95% CI: 1.05–1.98, p = 0.023) were significant positive predictors of better performance of dispatcher. In contrast, contractual employment (AOR = 0.37, 95% CI: 0.21–0.68, p = 0.001), higher monthly overtime hours (AOR = 0.33, 95% CI: 0.16–0.68, p = 0.002), and non-participation in stress management workshops (AOR = 0.40, 95% CI: 0.23–0.68, p = 0.001) were negatively associated with dispatcher performance. Complete airway obstruction (AOR = 0.08, 95% CI: 0.03–0.17, p < 0.001), occurrence in an outdoor setting (AOR = 4.86, 95% CI: 1.60–16.40, p = 0.007), and involvement of non-edible foreign bodies (AOR = 0.16, 95% CI: 0.06–0.37, p < 0.001) were significant predictors of outcomes.

Conclusion: The findings indicate that dispatcher performance in telephone-assisted management of FBAO was generally acceptable. Enhancing performance requires not only simulation-based training but also consideration of individual and occupational factors affecting dispatchers. Moreover, improving patient outcomes depends on dispatchers’ awareness of patient-specific characteristics to ensure timely and context-appropriate interventions.

Introduction: While methadone maintenance therapy (MMT) is well established in opioid use disorder, its potential modulatory effects on brain network dynamics in methamphetamine use disorder (MUD) remain unclear. This study aimed to evaluate the association of MMT with normalization of disrupted brain network activity in MUD.

Methods: In this cross-sectional study, resting-state functional magnetic resonance imaging (MRI) data were obtained from healthy controls, untreated methamphetamine users, and methamphetamine users receiving MMT who also met diagnostic criteria for opioid use disorder and were enrolled in supervised MMT programs. Dynamic functional connectivity was assessed using dynamic independent component analysis within the CONN toolbox. Clinical status was evaluated using the Positive and Negative Syndrome Scale (PANSS) and Brief Assessment of Cognition in Schizophrenia (BACS). Group comparisons were performed using general linear models controlling for age, education level, duration of methamphetamine use, and head-motion parameters.

Results: 110 participants (35 healthy controls, 45 untreated individuals with methamphetamine use, and 30 individuals with methamphetamine use receiving MMT) were studied. Cognitive performance assessed using BACS differed significantly across groups (p = 0.002, η² = 0.332), with untreated methamphetamine users demonstrating the lowest scores (19.88 ± 6.20), healthy controls the highest scores (30.38 ± 5.85), and MMT-treated participants showing intermediate performance (22.48 ± 7.61). Compared with untreated methamphetamine users, MMT-treated participants showed higher BACS scores, although the methamphetamine versus MMT effect was small-to-moderate (Cohen’s d = -0.38, 95% confidence interval (CI): -0.85 to 0.08).

PANSS positive, negative, and general psychopathology scores differed significantly across groups (all p < 0.001), with large effect sizes (η² range = 0.802–0.913). Untreated methamphetamine users exhibited the greatest symptom severity (Cohen’s d = 2.74, 95% CI: 2.10–3.38), whereas MMT-treated participants showed lower symptom burden than untreated users (Cohen’s d = 2.26, 95% CI: 1.67–2.85), although scores remained elevated relative to healthy controls (d = 4.22, 95% CI: 3.34, 5.11). The methamphetamine versus MMT effect sizes were large for PANSS positive (Cohen’s d = 2.74, 95% CI: 2.10–3.38), PANSS negative (Cohen’s d = 2.26, 95% CI: 1.67–2.85), and PANSS general psychopathology (Cohen’s d = 1.64, 95% CI: 1.11–2.18).

Conclusion: Methamphetamine use is associated with disrupted and unstable brain network dynamics that may underlie acute neurobehavioral disturbances seen in emergency settings. MMT was associated with partial stabilization of these alterations, suggesting a potential neurobiological benefit beyond opioid substitution. Persistent network abnormalities, however, indicate the need for adjunctive therapeutic strategies. Dynamic connectivity measures may offer a novel biomarker for risk stratification and management of substance-related emergencies.

Introduction: Attention-Deficit/Hyperactivity Disorder (ADHD), with a prevalence of about 5% in adults, is associated with risky behaviors and increased injuries. This study was conducted to investigate the ADHD prevalence and its related factors among individuals injured in traffic crashes.

Methods: This population-based cross-sectional study included the drivers who presented to the emergency department due to traffic crash injuries during one year. Data were collected through clinical interviews, review of medical records, and the Conners’ Adult ADHD Rating Scales (CAARS) questionnaire. Descriptive statistics were used to summarize demographic characteristics and inferential tests such as chi-square and logistic regression were applied to assess associations between ADHD diagnosis and crash-related variables.

Results: 450 drivers injured in traffic crashes in the year 2024 were examined for the prevalence of ADHD. Total prevalence of ADHD among studied drivers was 17.35%. ADHD was more prevalent among younger individuals (p = 0.0235) and males (p = 0.0007). Patients with history of previous crashes were significantly more likely to have ADHD (p = 0.0009). No significant association was found between ADHD and educational level (p = 0.9116), daily (p = 0.443) or weekly (p = 0.076) driving hours, type of vehicle (p = 0.522), or location of the crash (p = 0.825).

Conclusion: Based on the main finding the prevalence of ADHD among the studied derivers was 17.35%. The most important related factors of ADHD prevalence were younger age, male gender, and positive history of previous traffic crash injuries.

Introduction: Corticosteroids are widely used in COVID-19 pneumonia; however, the optimal regimen remains uncertain. We compared clinical outcomes between pulse-dose methylprednisolone and prolonged low-dose dexamethasone in hospitalized patients with COVID-19 pneumonia. Methods: In this retrospective cohort study, adult patients hospitalized with COVID-19 pneumonia (18 April–30 August 2021), who received either pulse-dose methylprednisolone or prolonged low-dose dexamethasone, were compared regarding 28-day mortality, in-hospital mortality, and treatment-free days. Multivariable Cox regression and propensity score matching (PSM) were used to adjust for confounding. Results: Among 460 patients (269 pulse-dose methylprednisolone, 191 prolonged low-dose dexamethasone), no significant difference in 28-day mortality was observed in multivariable analysis (aHR: 1.51; 95% CI: 0.74–3.09, p = 0.26). After PSM, 28-day mortality remained similar (HR: 1.40; 95% CI: 0.64–3.06; p = 0.38), although a trend toward higher in-hospital mortality was noted with pulse-dose methylprednisolone (HR: 2.07; 95% CI: 0.96–4.42; p = 0.06). Conclusion: Pulse-dose methylprednisolone was not associated with a significant difference in 28-day mortality compared with prolonged low-dose dexamethasone. A trend toward higher in-hospital mortality was observed with pulse-dose methylprednisolone, warranting cautious interpretation due to potential residual confounding.

Introduction: Although comorbidities are recognized as important prognostic factors in burn patients, the impact of individual comorbidities and associated trauma on treatment outcomes has not been fully elucidated. This study aimed to evaluate the associations between individual comorbidities, the presence of comorbidities, associated trauma, and treatment outcomes in adult burn patients. Methods: A retrospective cohort study was conducted among all eligible adult burn patients (aged ≥18 years) admitted to Le Huu Trac National Burn Hospital, Vietnam, between January 2021 and December 2025. The primary outcome was in-hospital mortality. Demographic characteristics, burn characteristics, comorbidities, and associated trauma were extracted from the electronic medical records. Univariate and multivariate regression analyses were performed to identify factors independently associated with the study outcomes. Results: The study included 6653 adult burn patients with median age of 40 (interquartile range (IQR): 31 – 52) years (73.94% male). Among adult burn patients, hypertension and diabetes mellitus were the most common comorbidities (3.01% and 2.80%, respectively). The presence of comorbidities was an independent predictor of both complications and mortality (odds ratio (OR) = 1.926, 95% confidence interval (CI): 1.180–3.144, p = 0.009; and OR = 1.785, 95% CI: 1.057–3.014, p = 0.030, respectively), along with age, total burn size, full-thickness burn size, and inhalation injury (all p < 0.001). Chronic pulmonary disease (OR = 11.371, 95% CI: 2.972–43.498, p < 0.001) and paralysis (OR = 3.451, 95% CI: 1.085–10.976, p = 0.036) remained independent predictors of mortality. Associated trauma was independently associated with prolonged hospital stay (p = 0.033). Conclusions: Comorbidities were independently associated with both complications and mortality in adult burn patients. Chronic pulmonary disease and paralysis were identified as independent prognostic factors for mortality. Although associated trauma was not independently associated with mortality, it was independently associated with a prolonged hospital stay.

Introduction: Particularly in highly tourist-active coastal locations, drowning is still a serious international public health concern.  This study investigates the predictive value of machine learning approaches in estimating drowning-related mortality risk.

 Methods:  This retrospective cohort study analyzed drowning incident data from the Emergency Management and Medical Urgency Center of Guilan Province, covering the period from 2018 to 2023.  The data were preprocessed, missing values imputed using the K-Nearest Neighbors (KNN) algorithm, and balanced using the Synthetic Minority Over-sampling Technique (SMOTE). Three models including logistic regression, decision tree, and naïve Bayes were evaluated in predicting the risk of mortality following drowning and sensitivity, specificity, and accuracy of each model was calculated and compared.

Results: A total of 600 consecutive cases meeting the eligibility criteria were extracted for analysis, forming the final dataset. Logistic regression exhibited the highest predictive power, with an accuracy of 51.67% and an area under the curve (AUC) of 60.02%. The most influential variables in drowning-related mortality prediction were drowning location, drowning year, gender, and age. High-risk areas posed a 33-fold higher mortality risk than safe locations (p < 0.001). Age and gender were not statistically significant predictors of fatal drowning.

Conclusion: Given its superior interpretability and predictive capability, logistic regression was identified as the most effective model for assessing drowning mortality risk. Preventative measures should focus on identifying high-risk areas, installing warning signs, implementing lifeguard teams, educating tourists, and enforcing strict coastal safety regulations to mitigate drowning fatalities.

Introduction:  Augmented reality (AR) and gamification have shown promise in medical education. This study aimed to examine their combined application in surgical instrument training for medical students.

Methods: This 2×2 factorial randomized assignment involved 164 operating room students from two universities. Participants were categorized by university and randomly allocated to four intervention groups: Group A (AR + game-based learning; n = 41), Group B (AR only; n = 41), Group C (game only; n = 41), or Group D (traditional instruction; n = 41). The 3-week intervention comprised one 30-minute session per week. Outcomes included knowledge retention (assessed at baseline, 1 week, 1 month, and 3 months) and Objective Structured Clinical Examination (OSCE) performance, evaluating surgical suite recognition, instrument arrangement, instrument use, and time efficiency.

Results: Group A (AR + game) showed better knowledge retention, with scores increasing from 44.7 ± 2.2 at baseline to 84.4 ± 2.7 at 1 week and remaining stable at 3 months (71.6 ± 1.9). Group D (traditional instruction) performed worst (56.7 ± 6.8 at 1 week; 48.6 ± 6.7 at 3 months). Total OSCE scores showed a similar trend: Group A scored 86.5 ± 4.3 out of 97, compared with Group D (62.7 ± 6.2). Factorial analysis confirmed significant benefits for AR versus non-AR (knowledge gain: 76.4 ± 9.1 vs. 62.3 ± 8.7; p < 0.001) and game versus non-game interventions (75.7 ± 8.9 vs. 63.0 ± 9.3; p < 0.001). Gender effects were sporadic and explained minimal variance.

Conclusions: The integration of AR and gamification appeared to be the most effective approach in this study for improving knowledge retention and procedural proficiency in surgical instrument training. Gamification alone showed stable long-term retention and may serve as a cost-effective alternative when AR integration is not feasible.

Introduction: Mechanical chest compression devices provide consistent depth and reduced pauses during cardiopulmonary resuscitation (CPR), but their clinical impact on routine practice in emergency department (ED) remains uncertain. This study aimed to compare the outcomes of mechanical versus manual compressions among adults with in-hospital cardiac arrest managed in ED.

Methods: A single-center, comparative study of consecutive adult cardiac arrests in the ED (n = 372) was carried out. Patients were allocated by time period to either manual CPR (n = 195) during the retrospective phase (September 2024 to January 2025) or mechanical CPR (n = 177) with LUCAS-3 during the prospective phase (January to June 2025). The primary outcome was return of spontaneous circulation (ROSC). Secondary outcomes were survival at 6 hours and 24 hours post-arrest. Baseline differences were summarized with standardized mean differences, and survival was described with Kaplan-Meier curves (0-24 h). Logistic regression estimated odds ratios (ORs) for ROSC and 6-hour survival.

Results: Mechanical and manual chest compression groups comprised 177 and 195 patients, respectively. Unadjusted outcomes favored mechanical CPR. ROSC occurred in 54 (30.5%) versus 32 (16.4%), with an absolute risk difference of 14.1% and Six-hour survival was 25 (14.1%) versus 5 (2.6%). After adjustment, mechanical CPR remained associated with higher odds of ROSC (OR = 2.44, 95% confidence interval (CI): 1.18-4.42) and 6-hour survival (OR = 6.71, 95% CI: 2.94-18.94). By 24 hours, no patient survived in the mechanical group, whereas one patient (0.5) survived in the manual group (P>0.05). Kaplan-Meier curves showed early separation that narrowed by 24 hours.

Conclusion: It seems that mechanical chest compression during CPR is associated with increased ROSC and better early survival, compared to manual compression. Due to the limited sample size, non-randomized design with time-based allocation, single-center setting, potential residual confounding, and absence of neurologic outcomes, these results should be interpreted with caution.

Introduction: The field of telemedicine has become an urgent innovation in the healthcare field worldwide, but there is still an unequal distribution of its implementation in transitional economies. This study aimed to evaluate the associated factors of telemedicine adoption in selected countries of the commonwealth of independent states (CIS) region.

Methods: A validated survey was used to collect data on 600 healthcare professionals, patients, information technology (IT) specialists and policymakers from selected CIS countries, using a mixed-method design. Through a designed and validated questionnaire, solid statistical techniques, and cross-regional analyses, the barriers and facilitators of telemedicine adoption in studied countries were evaluated.

Results: The general average score of telemedicine adoption was 3.84 ± 0.92. The highest mean adoption score was observed in Azerbaijan (4.02 ± 0.85), Russia (3.91 ± 0.88) and Ukraine (3.87 ± 0.91). There were significant differences between regions regarding mean adoption score (p < 0.001). Clinician acceptance (r = 0.64; p < 0.01), infrastructure readiness (r = 0.58; p < 0.01), regulatory maturity (r = 0.42; p < 0.01), and patient digital literacy (r = 0.36; p < 0.01) had the strongest correlation with telemedicine adoption. The most predictive factors of telemedicine adoption were infrastructure readiness (β (standard error; SE) = 0.42 (0.05), p < 0.001), then clinician acceptance (β (SE) = 0.39 (0.06), p < 0.001), patient digital literacy (β (SE) = 0.22 (0.05), p < 0.001), and regulatory maturity (β (SE) = 0.18 (0.04), p < 0.001). Professional experience had a minor yet significant impact (β = 0.09, t = 0.038). Logistic regression showed increased infrastructure readiness score (odds ratio (OR) = 1.48, 95% confidence interval (CI) = 1.21-1.81), clinician acceptance score (OR = 1.56, 95% CI = 1.28-1.92), regulatory maturity score (OR = 1.31, 95% CI = 1.09-1.58), and patient literacy score (OR = 1.22, 95% CI = 1.03-1.45) as the predictors of high telemedicine adoption (≥70%). The model accurately categorized 78.2% of data and the area under the curve 0.79 (95% CI: 0.75–0.83) meaning the model is a strong predictor.

Conclusion: The findings showed that the structural investments cannot be made alone without the involvement of professionals. The research contributes to the existing body of transitional economies research by offering strong comparative evidence of telemedicine and provides policy recommendations on how to improve infrastructure, generate harmonization, and capacity building of clinicians and patients to support sustainable digital health ecosystems.

Introduction: The safety of early antiplatelet administration versus withholding in patients with concurrent upper gastrointestinal bleeding (UGIB) and coronary artery disease (CAD) or myocardial infarction (MI) remains unresolved. This study aimed to evaluate the association between early antiplatelet therapy timing and mortality in this population.

Methods: This retrospective cohort study included 3,422 adults with UGIB and concurrent CAD or MI from the Medical Information Mart for Intensive Care IV (MIMIC-IV) dataset (2008-2019). Patients were classified as having antiplatelet therapy ordered within 24 hours (AP-24h; n=1,530), between 24 and 72 hours (AP-24-72h; n=368), or no antiplatelet therapy within 72 hours (No-AP; n=1,524; reference). This study quantified immortal time bias using 72-hour mortality distributions, applied landmark analysis to eliminate this bias, and externally validated findings using the eICU Collaborative Research Database (eICU-CRD). After excluding 87 patients dying within 72 hours, the MIMIC-IV landmark cohort comprised 3,335 patients. The primary outcome was 1-year all-cause mortality from the 72-hour landmark point. eICU-CRD external validation applied the same phenotype and exposure timing; after excluding 137 early deaths, the validation cohort included 2,657 patients. The eICU-CRD outcome was post-landmark hospital mortality.

Results: Landmark analysis demonstrated a 72-hour mortality rate of 3.7% in No-AP versus 1.8% in AP-24h, confirming misclassification of early critically ill patients into the reference group. Adjusted landmark Cox regression showed no significant difference in 1-year all-cause mortality for AP-24h (hazard ratio (HR): 0.910, 95% confidence interval (CI): 0.806-1.027, p=0.125) or AP-24-72h (HR: 0.884, 95% CI: 0.733-1.066, p=0.197) compared with No-AP. In-hospital mortality was similar across groups (8.7-9.8%); adjusted logistic regression showed no significant differences for AP-24h (OR: 1.056, 95% CI: 0.786-1.419, p=0.718) or AP-24-72h (OR: 1.007, 95% CI: 0.646-1.568, p=0.976). Thirty-day mortality was also similar for AP-24h (HR: 0.934, 95% CI: 0.780-1.118, p=0.455) and AP-24-72h (HR: 1.027, 95% CI: 0.785-1.343, p=0.847). In the eICU-CRD validation cohort, post-landmark hospital mortality was 8.69%, 5.07%, and 8.00% for AP-24h, AP-24-72h, and No-AP, respectively. Fully adjusted validation models showed no increased in-hospital mortality for AP-24h (OR: 0.97, 95% CI: 0.62-1.53, p=0.895) or AP-24-72h (OR: 0.74, 95% CI: 0.32-1.69, p=0.469).

Conclusion: After correcting for immortal time bias using landmark analysis, early antiplatelet administration in UGIB patients with CAD or MI was not associated with increased 1-year all-cause mortality or in-hospital mortality in the MIMIC-IV dataset, and this finding was externally supported in the eICU-CRD dataset.

Introduction: Mass casualty incidents create sudden surges in critical care demand that can overwhelm hospital capacity. This study aimed to develop consensus-based ICU admission criteria for mass casualty incidents. Methods: This methodological study employed a two-stage sequential design to identify, refine, and prioritize ICU admission criteria for mass casualty incidents (MCIs). First, an expert panel of 15 specialists in critical care, emergency medicine, and disaster management refined a preliminary checklist derived from a scoping review. Then, a two-round Delphi survey was conducted with 20 experts to assess consensus, and the study was reported in accordance with the CREDES recommendations. Consensus was predefined as a mean score of at least 4.0 on a five-point Likert scale. The response rate was 100%. Results: The initial checklist, which consisted of 46 criteria, was expanded to 51 items after the expert panel review. Following a two-round Delphi survey, consensus was reached on 18 criteria across six components: respiratory, cardiovascular, neurological, trauma and bleeding, multisystem, and special clinical considerations. The mean scores for these criteria ranged from 4.00 to 4.55, with the highest score being for respiratory rate at 4.55. Additionally, all retained items had an interquartile range of 1.00 to 1.25, indicating strong agreement among the experts. Conclusions: The proposed framework may serve as a preliminary decision-support tool for ICU triage in mass casualty incidents, based on expert consensus rather than clinical outcome validation. It may support equitable resource allocation and disaster preparedness, but external validation in other healthcare settings is required before broader application.

Introduction: By reducing the need for certain in-demand services, the virtual clinics could increase overall healthcare accessibility and operational effectiveness. This study aimed to examine the effect of establishing these clinics on the frequency of emergency department (ED) and primary healthcare (PHC) visits across Saudi Arabia.

Methods: A nationwide cross-sectional survey was carried out on 10,000 patients who had used virtual clinics in various parts of the country, between May 2024 and October 2025. A self-administered questionnaire was used in the study to collect information on the frequency and reasons for ED and PHC visits both before and after the introduction of virtual clinics. Fisher's exact tests, Pearson's chi-squared, and descriptive statistics were used in the data analysis process.

Results: Among the participants, 44% used virtual clinics once, 23% twice, and 33% more than twice. The establishment of virtual clinics led to 20% decrease in ED visits and 18% decrease in PHC visits. Preferred services included general consultations (76%), prescription renewals (49%), and follow-up appointments (48%). Factors contributing to reduction in in-person visits were shorter wait times (45.18%), better accessibility (39.61%), and reduced costs (36.23%). Demographic factors such as age, gender, and income level significantly influenced virtual clinic utilization patterns (p < 0.001).

Conclusion: Virtual clinics have raised operational efficiency while simultaneously enhancing the accessibility and quality of healthcare. The findings align with Saudi Arabia's Vision 2030 objectives on the digitization of the healthcare sector.

Introduction: Admission lactate and procalcitonin (PCT) are routinely measured in pediatric sepsis; however, their prognostic performance may vary by clinical phenotype. This study aimed to evaluate the shock-stratified prognostic performance of admission lactate and PCT for predicting in-hospital mortality in children with sepsis.

Methods: In this retrospective cohort study (July 2022 - July 2025), children aged 2 months to 15 years evaluated for suspected sepsis were screened; 193 met eligibility criteria and were included in analyses. Septic shock was recorded at admission, and organ dysfunction was recorded at admission or within the first 24 hours. The Phoenix Sepsis Score was calculated in 143 patients with complete lactate and other required data, whereas the Pediatric Sequential Organ Failure Assessment (pSOFA) score was calculated for all 193 patients using data recorded within the first 24 hours. Admission lactate and PCT were summarized by survival status within shock and non-shock phenotypes. Multivariable logistic regression models adjusted for age and malnutrition assessed biomarker-mortality associations and biomarker × shock interactions. Discrimination was evaluated using shock-stratified receiver operating characteristic (ROC) curves and area under the curve (AUC), with DeLong tests comparing AUCs between phenotypes.

Results: Among 193 children, 93 (48.2%) had septic shock, 145 (75.1%) had organ dysfunction, and 79 (40.9%) died in hospital. The median Phoenix Sepsis Score was 2.0 (range, 0–12) among 143 patients, while the median pSOFA score was 4.0 (range: 0–21) among all 193 patients. Mortality was higher in shock than non-shock sepsis (66.7% vs 17.0%; odds ratio (OR): 9.77 (95% confidence interval (CI): 4.96-19.24)) and in those with organ dysfunction (53.1% vs 4.2%; OR = 26.03 (95% CI: 6.09-111.34)). Admission lactate was higher in non-survivors than survivors in both non-shock and shock groups (p<0.05). In adjusted interaction models, each 1 mmol/L increase in lactate (OR = 1.50, 95% CI: 1.11–2.01; p = 0.0075) and each one-unit increase in natural-log–transformed PCT (ln(PCT)) (OR = 1.34, 95% CI: 1.01-1.79; p=0.0458) were associated with mortality in non-shock sepsis. The lactate × shock interaction (OR = 0.76, 95% CI: 0.55–1.05; p = 0.092) and ln(PCT) × shock interaction (OR = 0.74, 95% CI: 0.52–1.07; p = 0.114) were not statistically significant. Shock-stratified ROC curve analysis showed better discrimination in non-shock than shock for lactate (AUC: 0.76 (95% CI: 0.67-0.85) vs 0.62 (95% CI: 0.40-0.83); DeLong p=0.233) and modest discrimination for natural-log-transformed PCT [ln(PCT)] in both phenotypes (AUC: 0.65 (95% CI: 0.55-0.75) vs 0.61 (95% CI: 0.41-0.80); DeLong p=0.695).

Conclusion: Admission lactate showed moderate discrimination for in-hospital mortality in non-shock sepsis, whereas discrimination was lower in septic shock. PCT showed modest discrimination across phenotypes. Although AUCs were numerically higher in non-shock sepsis, between-phenotype differences were not statistically significant, supporting phenotype-based interpretation and the need for external validation.

Introduction: One of the preventable contributors to trauma mortality is hemorrhagic shock, which requires early recognition and immediate intervention. In this retrospective analysis, we aimed to develop and optimize machine learning (ML) algorithms to predict the need for packed red blood cell (PRBC) transfusion within 24 hours of injury in multiple trauma patients.

Methods: This retrospective longitudinal study analyzed consecutive multiple trauma patients admitted to the emergency department. The outcome was transfusion of at least one unit of PRBC within the first 24 hours of traumatic injury. SHAP analysis was employed for feature selection, and the five key predictors were identified and entered in the models: Glasgow Coma Scale (GCS), hemoglobin (Hb), pulse rate (PR), systolic blood pressure (SBP), and pulse pressure. The dataset was split 80:20 for training/testing, and multiple machine learning algorithms were evaluated based on area under the receiver operating characteristic curve (AUC), F1 score, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

Results: The study cohort consisted of 908 patients, with a median age of 34 years. PRBC transfusions were more common in older adults with lower GCS scores, higher PR, lower SBP, lower pulse pressure, and lower Hb levels on admission. Among the machine learning models, Random Forest performed best (AUC: 0.997, sensitivity: 0.938, specificity: 0.994), followed by K-Nearest Neighbors and Logistic Regression, both of which showed perfect specificity but lower sensitivity.

Conclusion: Random Forest outperformed other ML algorithms, achieving high discriminative ability, sensitivity, and specificity. PR, GCS, Hb, SBP, and pulse pressure were the most influential predictors of the need for early transfusion. Despite promising results, further multicenter validation studies are needed to confirm the real-world applicability of these models. 

Introduction: Non-contact frontal infrared thermometers (NCFITs) have gained popularity following the COVID-19 pandemic, but their accuracy remains debated. This study compared the accuracy of three index tests of patient-perceived fever (PPF), manual fever checking (MFC) by emergency nurses, and NCFIT against tympanic thermometer (TT).

Methods: This single center, diagnostic accuracy study evaluated adult patients in the triage area of the emergency department (ED) between May and September 2023. Five emergency nurses (ENs) examined patients using convenience sampling with index tests, followed by TT measurement for fever detection as standard test (T≥38°C). The sensitivity, specificity, positive and negative likelihood ratios (PLR and NLR) and accuracy, of PPF, MFC, and NCFIT in detecting the fever were measured and reported with 95% confidence interval (CI). The inter-rater reliability of ENs for MFC among 10 patients (outside the study) was 100%.

Results: We included 279 patients with the mean age of 52.2 ± 16.98 years (51.97% male). TT identified 147 as febrile and 132 as non-febrile. The most sensitive index test was MFC, achieving 100% (95%CI: 97.5–100.0) sensitivity, 0 NLR, and 8.25 (95%CI: 5.7–12.1) PLR, with moderate accuracy (93.9% (95%CI: 89.9–96.0)). NCFIT demonstrated the lowest sensitivity (85.71% (95%CI: 78.9–90.9)) but the highest specificity (98.48% (95%CI: 93.6–99.5)) and accuracy (97.5% (95%CI: 93.1-99.8)). When the fever threshold for NCFIT was lowered to 37.5°C, sensitivity increased to 99% (95%CI: 97.4-99.9), NLR decreased to 0.01 (95%CI: 0-0.6), and PLR rose to 49.5 (95%CI: 20.8-90.2), while specificity remained at 98% (95%CI: 94-99.6).

Conclusion: MFC/PPF could rule out fever but required NCFIT/TT to rule it in. However, NCFIT achieved the highest NLR and PLR when its fever threshold was set at 37.5 °C.

Introduction: Despite effective vaccines and post-exposure prophylaxis (PEP), gaps in awareness and practices hinder Rabies control efforts. This study assessed knowledge, attitudes, practices, and perception of healthcare professionals and community members regarding rabies control.

Methods: A cross-sectional study was conducted from May 2024 to April 2025 among community people who presented to emergency department (ED) with animal bite and health care professionals who were working in EDs of 4 Hospitals in Sari, Iran. Data were collected using validated questionnaires and analysed with descriptive and inferential statistics. The correlation between studied variables and knowledge, attitudes, practices, and perception of healthcare professionals and community members regarding rabies control were evaluated.

Results: 766 participants (383 healthcare professionals, 383 community members) were included. Community knowledge was low, with only 20.1% aware that animal bites can transmit rabies, compared to 98.7% of healthcare workers. Awareness of wound washing with soap and water was inadequate (31.3% among community members; 61.1% among healthcare professionals). Misconceptions about rabies hosts were common. A minority in both groups still relied on traditional healers, delaying appropriate care. Education, pet ownership, and access to clinics were positively correlated with higher knowledge, attitude and practice (KAP) scores.

Conclusions:  The study found significant shortcomings among healthcare professionals as well as severe gaps in rabies-related knowledge and essential preventive behaviors, especially among community members. Inadequate wound-washing techniques and a lack of knowledge about the routes of transmission underscore the critical need for focused education and ongoing professional training to enhance rabies control.

Introduction: Endotracheal intubation is a life-saving procedure, but it can result in complications such as hypotension, hypoxia, and cardiac arrest. This study aimed to identify physiological factors associated with difficult intubation in emergency departments (ED), in order to derive an exploratory bedside risk score.

Methods: This retrospective cohort study included patients aged ≥15 years who underwent endotracheal intubation in the ED. Pre-intubation physiological variables, procedural characteristics, and peri-intubation severe adverse events (severe hypoxemia, hypotension, and cardiac arrest) were extracted from standardized intubation records and electronic medical records. Independent physiological predictors of peri-intubation severe adverse events were extracted using logistic regression analysis, and an exploratory point-based bedside risk score was derived and internally validated.

Results: Among 720 eligible patients, 137 (19.0%) experienced peri-intubation severe adverse events. Five clinically relevant physiological predictors were retained in the exploratory score: suspected sepsis, pre-intubation hypoxemia, low serum bicarbonate level, vasopressor requirement before intubation, and shock index >0.9. The simplified score ranged from 0 to 11 points and showed modest discrimination, with an optimism-corrected C-statistic of 0.686. At a threshold of ≥5 points, the score had limited sensitivity (32.1%) but high specificity (90.1%), with a positive predictive value of 43.1% (95% confidence interval (CI): 33.4–53.3) and a positive likelihood ratio of 3.23 (95% CI: 2.29–4.56).

Conclusion: The exploratory physiological difficulty score showed modest discrimination for peri-intubation severe adverse events. A threshold of ≥5 points identified a subgroup of patients who may require more preparation and closer monitoring before intubation. However, the score should not replace comprehensive clinical assessment and requires external validation.

Introduction: Behavioral indicators have been increasingly utilized in machine learning (ML) and deep learning (DL) frameworks to enable automated driver drowsiness detection (DDD). This study aimed to investigate the available evidence on the modeling frameworks, datasets, input modalities, and performance metrics used in DDD systems.

Methods: Studies were identified through systematic searches of PubMed, Scopus, Web of Science, and IEEE Xplore for English-language publications up to 31 August 2025. Eligible studies were original research that applied ML or DL techniques to image- or video-based behavioral features for automatic DDD. Studies focusing primarily on vehicle telemetry, physiological signals without behavioral imaging, reviews, editorials, and gray literature were excluded. We extracted the dataset, input data modality, driving context, inference mode, ML/DL methods, and performance metrics including accuracy, precision, recall, and F1-score. Risk of bias was evaluated using the PROBA-AI tool.

Results: A total of 69 studies met the inclusion criteria. DL models outperformed ML, achieving higher median accuracy (94.48% vs. 91.80%) and significantly better F1-scores (93.15% vs. 84.00%). Median recall was comparable between DL and ML models (93.76% vs. 94.12%), and precision remained similarly high across both approaches (93.18% vs. 94.6%). Most DL methods employed Convolutional Neural Networks (CNN), Recurrent Neural Network (RNN), or hybrid CNN– Long Short-Term Memory (LSTM) architectures, whereas ML studies primarily relied on classical classifiers such as Support Vector Machine (SVM) and Random Forest (RF) supported by handcrafted behavioral features. PROBA-AI assessment revealed considerable methodological heterogeneity, with 27 studies rated as high risk, 25 as low risk, and 17 as moderate.

Conclusions: DL models demonstrate clear performance advantages over ML approaches, particularly in accuracy and F1-score. However, substantial methodological variability-reflected in inconsistent dataset design, annotation practices, and validation strategies-continues to limit comparability across studies.

Introduction: Minocycline as a tetracycline derivative agent with anti-inflammatory and neuroprotective properties, has been investigated in both experimental and clinical settings. This study aimed to evaluated the therapeutic efficacy of Minocycline in acute traumatic spinal cord injury (SCI).

Methods: This systematic review and meta-analysis was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Major electronic databases were comprehensively searched to identify preclinical animal studies and human clinical studies evaluating minocycline, alone or in combination therapies, for acute traumatic SCI management. Risk of bias was assessed using Joanna Briggs Institute (JBI) critical appraisal tools tailored to study design. Qualitative synthesis included all eligible studies, while quantitative synthesis was restricted to clinical studies reporting extractable effect estimates for neurological improvement.

Results: A total of 9 studies met inclusion criteria for qualitative synthesis. Preclinical studies demonstrated consistent biological effects of minocycline on inflammatory markers, oxidative stress, and histopathological outcomes, particularly in combination therapies, although functional recovery with minocycline monotherapy was inconsistent. Clinical studies indicated that minocycline was generally well tolerated; however, most trials did not demonstrate statistically significant improvements in neurological or functional outcomes. Only two clinical studies provided suitable data for meta-analysis, yielding a pooled odds ratio of 1.70 (95% confidence interval (CI): 0.95–3.06) for neurological improvement, which did not reach statistical significance.

Conclusion: Current evidence suggests that while minocycline exhibits promising biological activity and an acceptable safety profile in acute traumatic SCI, robust clinical efficacy has not been conclusively demonstrated. Well-designed, adequately powered randomized controlled trials with standardized outcome reporting are required to determine whether these biological effects translate into meaningful functional recovery.

Introduction: Artificial intelligence (AI) models applied to non-contrast brain computed tomography (CT) scan have demonstrated promising performance in hematoma detection, segmentation, and outcome prediction. Explainable artificial intelligence (XAI) has been proposed as a strategy to improve transparency and facilitate clinical integration. This study aimed to map and summarize XAI methods used in CT scan-based studies of intracranial hemorrhage (ICH), and to assess their validation approaches, transparency, and clinical relevance.

Method: The scoping review was conducted in accordance with the PRISMA-ScR guidelines and was registered on the Open Science Framework (osf.io/5kxbt). In this review, original studies of adult and pediatric patients with spontaneous ICH that used AI or deep learning on non-contrast brain CT scan and included an explainability or interpretability method were eligible. Reviews, editorials, conference abstracts without full text, and studies without XAI components were excluded. PubMed, Embase, and Scopus were searched from inception to September 2025. Data were extracted on study design, imaging inputs, AI task, explainability technique, validation strategy, and reported clinical relevance.

Results: A total of twelve studies met inclusion criteria. Most investigations focused on hematoma detection, segmentation, or prediction of functional outcome or mortality. Gradient-based visualization techniques, particularly Grad-CAM and saliency maps, were the most commonly used explainability approaches. XAI outputs predominantly highlighted hematoma regions and perihematomal tissue; however, reporting and interpretation of explainability varied substantially across studies. External validation was infrequent, and few studies formally assessed alignment between XAI outputs and established radiological or clinical reasoning. The evidence was small, heterogeneous, and largely retrospective, with inconsistent reporting of explainability quality and limited external validation.

Conclusion: Explainable AI applied to non-contrast brain CT scan in ICH cases, is an evolving field with growing methodological diversity but limited standardization. While XAI techniques offer potential to enhance transparency and clinician confidence, current evidence remains insufficient to support routine clinical implementation. Future studies should emphasize standardized reporting, external validation, and clinically grounded evaluation of explainability outputs.

Introduction: There are a number of biomarkers connected to inflammation that affect spinal cord injury (SCI) outcomes. This systematic review and meta-analysis was conducted to evaluate the prognostic value of the Neutrophil-to-lymphocyte ratio (NLR) in patients diagnosed with traumatic SCI.

Methods: Observational studies evaluating the association between baseline NLR and severity or neurological improvement in traumatic SCI patients were systematically identified using Medline, Embase, Scopus, and Web of Science databases on March 4, 2025. The risk of bias among the included studies was assessed using the National Heart, Lung, and Blood Institute (NHLBI) tool. A random-effects model was employed for meta-analyses. Effects sizes were reported as odds ratios (ORs) or standardized mean difference (SMD), along with their 95% confidence intervals (CIs).

Results: Six retrospective cohort studies involving 1,564 participants were included, with five eligible for meta-analysis. Blood samples were collected within 72 hours of admission. Meta-analysis showed that patients without neurological improvement had significantly higher NLR values (SMD: 0.98; 95% CI: 0.08–1.87; I² = 94.36%). Additionally, elevated NLR was independently associated with no neurological improvement (adjusted OR: 1.12; 95% CI: 1.06–1.17; I² = 0.04%). Qualitative synthesis further indicated that higher NLR values were consistently associated with greater injury severity in acute traumatic SCI.

Conclusion: Elevated NLR is associated with greater injury severity and poorer neurological recovery in patients with acute traumatic SCI. These findings suggest that NLR may serve as a useful early prognostic biomarker in the clinical assessment of mentioned patients.

Background: DNA methylation markers have been proposed as molecular triage tools for detecting cervical precancer and cancer, particularly among HPV-positive women. However, diagnostic performance varies across markers, assays, specimens, and clinical settings. This study aimed to evaluate the marker-specific diagnostic accuracy of DNA methylation markers and methylation panels for detecting CIN2+ and CIN3+ in cervical cancer screening, triage, and related diagnostic contexts. Methods: This systematic review and diagnostic test accuracy meta-analysis was reported according to PRISMA-DTA. Marker-specific analyses were performed separately by endpoint to avoid double counting. Pooled sensitivity, specificity, diagnostic odds ratio, clinical utility estimates, risk of bias, publication bias, and certainty of evidence were assessed using diagnostic test accuracy methods, QUADAS-2, Deeks' test, and GRADE-DTA. Results: The broad main diagnostic meta-analysis included 74 eligible studies, contributing 260 diagnostic test accuracy comparisons across 130 marker-endpoint-sample analyses. A strict sensitivity dataset included 164 comparisons from 51 studies. Seven marker analyses were available for CIN2+ and seven for CIN3+. Diagnostic performance varied by marker and endpoint. For CIN2+, S5 EPB41L3 plus HPV16/18/31/33 methylation showed the highest sensitivity but lower specificity, whereas PAX1, JAM3, SOX1, PAX1/JAM3, and Six-gene/GynTect showed higher specificity. For CIN3+, S5 showed the highest sensitivity, while PAX1/JAM3, JAM3, SOX1, PAX1, and Six-gene/GynTect had more balanced performance. Deeks' test did not show strong evidence of publication bias for most analyses, although borderline findings were observed in selected marker-endpoint analyses. GRADE-DTA certainty was low or very low across marker-endpoint analyses. Conclusions: DNA methylation markers showed heterogeneous diagnostic accuracy for detecting CIN2+ and CIN3+. Some markers demonstrated potentially useful sensitivity- or specificity-oriented profiles, but certainty of evidence was low or very low. Further standardized prospective validation is required before routine clinical implementation.

Introduction: Acute pain serves as a modifier in five-level triage systems, such as the Canadian Triage and Acuity Scale (CTAS) and the Korean Triage and Acuity Scale (KTAS). This systematic review aimed to synthesize existing evidence on the impact of pain on triage accuracy, hospital admission, and length of stay in patients presenting to the emergency department (ED).

Methods: A systematic search was conducted in PubMed and Google Scholar for English studies published between 2000 and 2026. Studies that reported associations between pain score, triage accuracy, hospital admission, and length of stay in patients presenting to the ED were included. Data extraction and quality assessment were performed according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines.

Results: Out of 484 initial records, independent screening yielded 6 original cohort studies for final inclusion. The studies encompassed a total of 247,624 patients, with moderate to high methodological quality in the qualitative synthesis. Self-reported pain scores demonstrated no predictive relationship with admission rates or ED length of stay. However, their mandatory integration into triage protocols resulted in over-triage and reduced system accuracy. Based on the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach, the overall certainty of evidence was determined to be moderate.

Conclusion: Self-reported pain lacks independent predictive value for hospital admission or ED length of stay, and its mandatory integration into triage systems leads to over-triage. Rather than determining acuity priority, pain assessment should be decoupled from the primary triage decision and be linked to dedicated pain-management protocols instead.

Introduction: Electroencephalography (EEG) and quantitative electroencephalography (QEEG) may provide noninvasive functional biomarkers that complement clinical and imaging predictors when evaluating stroke outcomes. This systematic review evaluated QEEG, selected routine EEG, processed EEG, and advanced neurophysiological biomarkers of severity, lesion burden, longitudinal prognosis, cognitive/language outcome, rehabilitation response, and recovery after ischemic stroke. Methods: PubMed, Embase, Scopus, and Web of Science were searched on 30 March 2026 using ischemic stroke or cerebral infarction terms, QEEG or EEG-derived biomarker terms, and prognosis, outcome, severity, infarct-volume, mortality, rehabilitation, motor-recovery, and language-recovery terms. Selected structured routine EEG, visual EEG grading, processed EEG/Bispectral Index (BIS), and EEG-derived neurovascular-coupling studies were included when they provided predefined severity, prognostic, monitoring, cognitive/language, rehabilitation, or recovery-related evidence. The search identified 2,564 records; 851 duplicates were removed, leaving 1,713 unique records for screening. Evidence was classified as cross-sectional severity or lesion-burden association, longitudinal functional prognosis, mortality or clinical deterioration, rehabilitation or motor recovery, cognitive/language outcome, selected routine or processed EEG evidence, or advanced QEEG/connectivity/nonlinear/neurovascular-coupling evidence. Risk of bias was assessed using Quality in Prognostic Studies (QUIPS)-oriented criteria, and certainty was summarized using domain-level Grading of Recommendations Assessment, Development and Evaluation (GRADE)-informed judgments. Results: Thirty-three study records were included, representing 1,696 stroke or suspected-stroke participants, 1,598 EEG/QEEG/BIS/neurovascular coupling (NVC)-analyzable participants, and 317 controls or comparators. The most consistent prognostic pattern was that greater EEG/QEEG abnormality, particularly increased slow-wave activity and higher delta/alpha ratio (DAR) or delta-theta/alpha-beta ratio (DTABR), was associated with greater neurological severity, larger infarct or lesion burden, and poorer functional outcomes. Measures of interhemispheric imbalance, including a higher brain symmetry index (BSI), pairwise-derived brain symmetry index (pdBSI), and greater hemispheric asymmetry, were also generally associated with unfavorable outcomes, including greater disability, clinical deterioration, mortality, or less favorable recovery. Reduced alpha or beta activity was similarly associated with poorer neurological or functional outcomes in several studies. Post-endovascular treatment (EVT)/mechanical thrombectomy (MT) studies suggested potential monitoring value for detecting or predicting cortical dysfunction, early neurological deterioration, cerebral edema, nonconvulsive seizures/status epilepticus, infarct growth, and unfavorable 90-day outcome. Rehabilitation and advanced QEEG studies suggested that motor/language recovery may relate to interhemispheric balance, phase synchrony, weighted node degree, nonlinear complexity, and neurovascular coupling. However, the evidence was heterogeneous, mostly exploratory, and generally low to very-low certainty; no universal prognostic threshold or pooled prognostic estimate was supported. Conclusions: EEG/QEEG abnormalities may provide candidate severity association, monitoring, prognostic, and recovery biomarkers after ischemic stroke. This review distinguishes cross-sectional severity or lesion burden associations from longitudinal prognostic, monitoring, and recovery outcomes. However, current evidence remains heterogeneous and mostly low to very low certainty and does not support a universal prognostic threshold or global pooled prognostic estimate.

Introduction: Creatine kinase (CK) has frequently been introduced as a predictive factor of rhabdomyolysis induced acute kidney injury (AKI) in several studies. However, its value, especially in traumatic and disaster settings, remains to be fully clarified. Methods: Online databases of Medline (via PubMed), Embase, Scopus, and Web of Science were systematically searched until May 15th, 2026 and studies evaluating the association between serum CK and AKI in traumatic rhabdomyolysis patients were included. Reports of CK were collected as mean differences between the AKI and non-AKI groups, odds ratios (ORs), and performance metrics such as sensitivity and specificity. Analyses were performed using STATA 17.0 statistical software. The study’s protocol has been registered with PROSPERO (CRD420251250906). Results: 17 articles were included in this study. Twelve studies encompassing 3122 rhabdomyolysis patients (AKI: 30.94%) evaluated mean CK values in AKI and non-AKI patients with a pooled mean difference of 21770.75 (95% CI: 8987.39 to 34554.11) IU/L between groups. Four studies of 3549 patients (AKI: 20.29%) reported a pooled adjusted OR of 7.62 (95% CI 2.80 to 20.76). Ten articles, including 3421 patients (AKI: 18.42%), reported prediction accuracy measures. The pooled analysis showed an AUC of 0.73 (95% CI: 0.69 to 0.77), with sensitivity 0.68 (95% CI: 0.55 to 0.78) and specificity of 0.68 (95% CI: 0.54 to 0.78). Conclusion: Our findings indicate that, across traumatic rhabdomyolysis populations, AKI was associated with higher CK levels, with a more pronounced elevation observed in earthquake-related settings, although heterogeneity was high. CK alone demonstrated only fair discriminatory performance, suggesting its use as part of a broader clinical risk assessment rather than as a sole predictor of AKI.

Introduction: Cardiopulmonary Resuscitation (CPR) is a critical emergency procedure that needs to be promptly administered to prevent irreversible damage and enhance the chances of survival. This systematic review and meta-analysis aimed to determine the success rate of CPR and the rate of survival to hospital discharge among patients who underwent CPR in hospitals throughout Iran.

Methods: This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines, and the protocol was registered in International Prospective Register of Systematic Reviews (PROSPERO) (CRD420251185276). A comprehensive search was conducted without time restriction until July 2026 in PubMed, Scopus, Web of Science, Google Scholar, SID, and Magiran databases. A random-effects model was used for meta-analysis, and heterogeneity was assessed using the I² index. Publication bias was evaluated using Begg’s test. Data analysis was performed using STATA software (version 14).

Results: Initially, 903 records were identified, and after removing duplicates and applying screening procedures, 47 studies met the inclusion criteria. The pooled analysis revealed that the rates of successful CPR, survival to discharge among successful CPR cases, and survival to discharge among all CPR attempts were 29.48% (95% confidence interval (CI): 25.04–33.92), 32.11% (95% CI: 24.00–40.21; I²=95.7%), and 8.63% (95% CI: 6.72–10.53), respectively. Meta-regression indicated an increasing trend over time for successful CPR, a decreasing trend for survival to discharge among cases with Return of Spontaneous Circulation (ROSC), and a significant increase in overall survival to discharge.

Conclusion: This systematic review highlights that while the initial quality of CPR in Iran is generally satisfactory, there is a notable gap between successful resuscitation and survival to hospital discharge, primarily attributed to limitations in post-resuscitation care. Enhancing post-CPR management, expanding ICU capacity, improving team coordination, and standardizing equipment could significantly impact patient outcomes.

Introduction: Accurate prediction of mortality following myocardial infarction (MI) is critical for timely identification of high-risk patients and optimization of interventions. Conventional statistical models are commonly used; however, advanced machine learning (ML) methods are being increasingly recognized. This meta-analysis aimed to systematically evaluate and compare the predictive performances of various ML models.

Methods A systematic search of the Medline (via PubMed), Embase, Scopus, and Web of Science databases was conducted up to January 9, 2025. A total of 14933 articles were identified, of which 330 underwent a full-text review and 69 met the inclusion criteria. The meta-analysis was conducted using a bivariate random-effects model in the ‘midas’ package of STATA 14. Subgroup analyses were conducted based on the follow-up duration and selected clinical features. The risk of bias was assessed using the QUAPAS. Publication bias and evidence certainty were assessed using Deeks' funnel plots and GRADE framework, respectively.

Results Gradient Boosting Machines (GBM), Single Decision Tree Models, and Random Forest models yielded similarly high predictive accuracies. Advanced GBMs, particularly XGBoost (AUC = 0.90, 95% CI: 0.87-0.92; sensitivity = 0.78, 95% CI: 0.74-0.82; specificity = 0.87, 95% CI: 0.83-0.89), showed the highest evidence certainty due to precision and minimal publication bias. Across advanced GBMs, adding echocardiographic parameters increased the sensitivity from 0.77 to 0.83 and specificity from 0.85 to 0.90, indicating a clinically meaningful yet resource-dependent gain in discrimination.

Conclusions Advanced Gradient Boosting Machines, particularly XGBoost, currently provide the most reliable mortality predictions in patients with MI. Future research should emphasize external validation, transparent reporting of feature selection, detailed data preprocessing, and dedicated studies on populations with NSTEMI.

Introduction: Stroke is the second leading cause of death globally, resulting in 5.5 million deaths annually. The ACT-FAST is a simplified, rapid assessment tool aimed at improving the early detection of stroke and may outperform existing tools in identifying large vessel occlusions (LVOs). We aimed to assess the diagnostic accuracy of this tool in this systematic review.

Methods: In this systematic review, we conducted a comprehensive search of Medline, Scopus, Web of Science, and Embase. Studies with incomplete data reporting or those focusing on other triage tools were excluded. Two independent reviewers screened titles, abstracts, and full texts, and extracted relevant data using a pre-designed checklist.

Results: Our systematic review identified four studies on the ACT-FAST tool, all conducted primarily in Australia with a predominantly elderly patient population. Reported findings indicate that ACT-FAST has demonstrated generally high diagnostic performance, with retrospective analyses showing accuracies above 90%. Sensitivity varied across study designs, ranging from 63% to 100%, while specificity was consistently reported above 85%. Despite concerns regarding potential bias from non-random patient selection and incomplete reporting of symptom onset, the overall methodological quality was robust.

Conclusion: The ACT-FAST algorithm demonstrates generally high, though variable, sensitivity and consistently high specificity, supporting its utility as a valuable tool for paramedics and clinicians in prehospital stroke management and for effectively identifying patients with LVO. This capability facilitates timely intervention, minimizes unnecessary transfers for patients without LVO, and optimizes resource allocation, ultimately improving patient outcomes in stroke care.

Introduction: Therapeutic hypothermia as a neuroprotective strategy can reduce secondary injury after acute traumatic spinal cord injury (SCI). We conducted a systematic review and meta‑analysis to evaluate the impact of systemic or local hypothermia on neurological outcomes, mortality, and Intensive Care Unit (ICU) stay of SCI patients.

Methods: A systematic search in PubMed, Scopus, Web of Science, Embase, and The Cochrane Central Register of Controlled Trials (CENTRAL) with no limitation of time and language was performed. Following PRISMA 2020 guidelines, two independent reviewers screened records across four databases up to 20 September 2025. Eligibility was determined using PICOS criteria: adults with acute traumatic SCI (P), receiving any hypothermic protocol (I), compared with standard normothermic management (C), with reported functional, sensory, or survival outcomes (O), and original human clinical designs (S). Data extraction and risk-of-bias evaluation were performed independently using the Joanna Briggs Institute (JBI) checklist. Pooled relative risks (RR) or mean differences (MD) were calculated with random‑effects models using RevMan 4.5.1

Results: From 305 initial records, six human studies (156 patients) met inclusion criteria. There were three systemic hypothermia trials and one local extradural protocol, plus two supportive cohorts providing timing and assessment data, but only three studies met the minimum criteria for meta-analysis processes. Overall methodological quality of included studies was low‑to‑moderate and none of the studies were randomized. Intervention methods included surface, and endovascular techniques maintaining body temperature at 32-34 Celsius for 24-72 hours, initiated between 1.6 -70 hours post‑injury. Pooled analyses showed decreased mortality with RR = 0.57 (95% CI: 0.05 -5.88; p = 0.6883), improvement in Association Impairment Scale (AIS) with RR = 2.96 (95% CI: 0.01- 939.31; p = 0.3098); and decrease in the ICU length of stay with MD = -1.27 (95% CI: -2.46 to -0.07; p = 0.9658) days in SCI patients receiving hypothermia. Complications included pneumonia, hypotension, and bradycardia. No hypothermia‑related deaths were reported. Early initiation (< 6 hours) was consistently linked with superior functional improvement.

Conclusions: The findings of six human studies reveal that therapeutic hypothermia may be a feasible, safe, and effective intervention in acute traumatic SCI. While current evidence cannot yet demonstrate mortality benefit, the observed results were directed toward neurological improvement, especially when therapy is initiated early and systemically, supporting ongoing investigation.

Introduction: Electroencephalography (EEG) and quantitative EEG (QEEG) are candidate functional biomarkers that may complement clinical examination and imaging for acute ischemic stroke triage and post-reperfusion monitoring. However, diagnostic targets, recording systems, thresholds, and post-treatment monitoring endpoints are heterogeneous. This review aimed to evaluate acute-care EEG applications in ischemic stroke by separately synthesizing evidence on pre-treatment diagnostic triage and post-EVT/MT monitoring. Methods: A systematic search was performed in PubMed, Embase, Scopus, and Web of Science on 4 February 2026, with supplementary backward and forward citation tracking. Eligible studies evaluated EEG, QEEG, processed EEG, visual EEG grading, or EEG-derived physiological markers for acute stroke/transient ischemic attack (TIA) classification, large-vessel occlusion (LVO)/medium-vessel occlusion (MeVO) detection, lesion-related classification, stroke-control/severity classification, or post-endovascular treatment/mechanical thrombectomy (EVT/MT) monitoring. Diagnostic components were interpreted in line with PRISMA 2020 and PRISMA-DTA principles. Risk of bias was assessed using QUADAS-2 for diagnostic/classification evidence and QUIPS-oriented criteria for post-EVT/MT monitoring studies. Results: From 4,796 database records, 3,179 unique records were screened, 51 full texts were assessed, and 15 study records were retained: seven diagnostic/classification studies and eight post-EVT/MT monitoring studies. Acute triage evidence suggested candidate value for EEG/QEEG in acute stroke/TIA or LVO/MeVO identification, with promising individual-study signals for slow-to-fast ratios, hemispheric asymmetry/pdBSI, relative alpha/theta power, and clinical-plus-EEG models. However, devices, settings, target conditions, thresholds, and validation status varied, and no diagnostic domain was poolable. No diagnostic meta-analysis was performed because target conditions, thresholds, reference standards, EEG systems, and intended-use settings were too heterogeneous. Post-EVT/MT monitoring studies suggested candidate value for QEEG slowing/frontal DAR, relative alpha loss, visual EEG grading, NCSE detection, BIS/NIRS, and EEG-TCD NVC in relation to END, edema, seizure-related complications, infarct growth, or functional outcome. Certainty was very low for diagnostic domains and low for post-EVT/MT monitoring. Conclusions: EEG/QEEG is a promising but still investigational adjunct for acute ischemic stroke triage and post-reperfusion monitoring. Current evidence does not support a universal diagnostic threshold, pooled diagnostic accuracy estimate, or stand-alone clinical implementation. Larger intended-use validation studies with complete diagnostic accuracy reporting and externally validated thresholds are required.

Introduction: Interruptions are common in emergency departments (ED) and frequently occur during critical clinical tasks. These interruptions disrupt workflow, reduce situational awareness, increase the risk of errors, and may compromise patient safety. Identifying and mapping their sources is therefore essential to guide targeted strategies and improve the quality of care. This scoping review aimed to identify and categorize the primary sources of work-related interruptions among physicians and nurses in the ED.

Methods: The review question, developed using the PCC framework (physicians and nurses; work-related interruptions; EDs), sought to identify the primary sources of interruptions in ED settings. A comprehensive search was performed in PubMed, Scopus, Embase, Web of Science, and Google Scholar to locate peer-reviewed studies published up to September 2025. Two reviewers independently screened the studies and extracted the data.

Results: A total of 12 studies met the inclusion criteria. Reported interruption rates varied across settings and professional roles. The primary sources of interruptions were grouped into six major categories: colleague interference, phone and pager alerts, patient-initiated interruptions, family or companion interference, technical failures and lack of resources, and environmental noise. Most interruptions occurred at nursing or physician workstations and during medication preparation, documentation, or direct patient care.

Conclusion: This scoping review provides a comprehensive overview of the main sources of interruptions in ED. Understanding where and why these interruptions occur can guide the development of targeted educational, organizational, and environmental interventions. Reducing avoidable interruptions may enhance staff performance, improve patient safety, and optimize the quality of emergency care.

Background: Emergency medical services (EMS) are critical for responding to crises and mass casualty incidents (MCIs). Rapid decision-making and effective patient flow management are essential for improving survival. Digital technologies—particularly dashboards—enable real-time monitoring of performance indicators and data-driven decision-making. This scoping review aimed to identify and classify key performance indicators (KPIs) used in EMS and to examine the role of dashboards in monitoring these indicators in both routine and crisis conditions. Methods: A systematic search was conducted in PubMed, Scopus, Embase, and Web of Science from inception to January 2026, supplemented by gray literature. Two independent reviewers screened titles/abstracts and full texts. Data were extracted using a standardized form and synthesized narratively in accordance with PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. Results: Twenty-six studies were included. KPIs were categorized using the Donabedian model into structure (resources), process (response time, triage, coordination), and outcome (survival, satisfaction). Time-based indicators were most frequent. Eight studies (31%) reported dashboards, all in routine operations. Eight studies (31%) used real-time feedback devices (not classified as dashboards), and ten studies (38%) employed other digital tools. Only one study addressed crisis conditions, and it did not use a dashboard. Conclusion: Dashboards are predominantly applied in routine EMS operations, with very limited evidence in crisis contexts. Developing crisis-specific, context-adaptive dashboards and standardized indicators should be prioritized to support decision-making and coordination in emergencies and disasters.

Introduction: There is no consensus on the diagnostic performance of ultrasonography for identifying skull fractures in children, and the accuracy of this imaging modality in this age group has been questioned. This study performed a pooled analysis to explore the diagnostic performance characteristics of ultrasonography in this regard.

Methods: Three databases, Web of Science, Medline, and Scopus, were systematically searched from their inception through August 2025. Data extraction of diagnostic parameters was performed by two independent authors. Pooled estimates of the diagnostic parameters were calculated using Meta-Disc software and the MIDAS package of Stata.

Results: The estimated sensitivity of ultrasonography for detecting skull fracture in children was 0.89 (95% confidence interval (CI): 0.86–0.92), and the specificity was 0.97 (95% CI: 0.96–0.98). The estimated positive likelihood ratio (PLR) was 23.21 (95% CI: 14.84–36.32), while the negative likelihood ratio (NLR) was 0.13 (95% CI: 0.09–0.20). The diagnostic odds ratio (DOR) was 208.89 (95% CI: 112.68–387.27), and the estimated overall diagnostic accuracy of ultrasonography was 0.96.

Conclusion: Ultrasonography demonstrated high diagnostic accuracy for identifying skull fractures in children, with evidence indicating stronger performance for confirmation than for exclusion. However, the high risk of bias in patient selection across the studies limits the generalizability of the evidence, and the results should therefore be interpreted with appropriate caution.

Background: The prevalence of acute kidney injury (AKI) associated with hematopoietic stem cell transplantation (HSCT) is well-established in the adult population. Similarly, AKI following HSCT is frequently encountered in the pediatric population. However, adequate information regarding the prevalence among children and adolescents is lacking. We conduct a systematic review to investigate the current literature specifically addressing the prevalence of AKI in children treated with HSCT.

Method: An extensive search was performed using Medline, Embase, Scopus, and Web of Science databases until May 5, 2025. We systematically reviewed and included all clinical trials and observational studies. Two independent researchers screened the studies, recorded the required data, and assessed the risk of bias. The studied outcomes included the prevalence of AKI and the need for renal replacement therapy (RRT). The data included were analyzed in the STATA 18.0 statistical program.

Results: Forty-nine articles were eligible for inclusion. The prevalence of pediatric AKI after HSCT was 34.18% (95% confidence interval (CI): 28.08 to 40.53; I2=97.79%). The need for RRT following HSCT was equal to 3.54% (95% CI: 1.98 to 5.45; I2= 95.10%). The origin of hematopoietic stem cells and AKI determination cutoff points were found to be the most predictive of AKI prevalence. The prevalence of AKI after transplantation of hematopoietic cells derived from umbilical cord blood was significantly higher than other cell sources (69.55%; 95% CI: 22.23, 100.0).

Conclusion: The prevalence of pediatric AKI and RRT after hematopoietic cell transplantation is significant, and is 34.18% and 3.54%, respectively. The highest prevalence of AKI among the different sources of hematopoietic stem cells was observed in cord blood transplantation. However, the available evidence on the prevalence of AKI following the transplantation of hematopoietic stem cells derived from cord blood is insufficient, and more research is needed in this field. As a suggestion, it is recommended to use sources other than hematopoietic cells derived from umbilical cord blood until sufficient evidence is available.

Introduction: Several studies have assessed the diagnostic accuracy of ultrasonography for detecting acute lateral ankle ligament injuries, but their findings have been inconsistent. This systematic review and meta-analysis investigated the pooled diagnostic performance of ultrasonography for early detection of acute injuries of the four lateral ankle ligaments.

Methods:  Major databases with a high likelihood of containing eligible studies, including Medline, Scopus, and Web of Science, were systematically searched from inception to April 2026. Diagnostic parameters were estimated using the extracted contingency table data, with MetaDisc software and the MIDAS package in Stata.

Results: 10 eligible studies, with a total of 454 patients, were included in the analyses. The sensitivity of ultrasonography for diagnosing injuries of the anterior talofibular ligament (ATFL), calcaneofibular ligament (CFL), posterior talofibular ligament (PTFL), and anterior inferior tibiofibular ligament (AITFL) were 0.95 (95% CI, 0.92–0.98), 0.82 (95% CI, 0.76–0.88), 0.33 (95% CI, 0.13–0.59), and 0.90 (95% CI, 0.74–0.98), respectively. The specificity for detecting injuries of the ATFL, CFL, PTFL, and AITFL was 0.91 (95% CI, 0.85–0.96), 0.90 (95% CI, 0.85–0.95), 0.96 (95% CI, 0.90–0.99), and 0.86 (95% CI, 0.78–0.92), respectively.

Conclusion: Ultrasonography demonstrates high diagnostic accuracy for detecting lateral ankle ligament injuries, particularly for the ATFL and AITFL, with strong sensitivity and specificity. It is also effective for both confirming and excluding ATFL injuries. For CFL injuries, ultrasonography shows good diagnostic performance, although it is more effective for confirming than excluding CFL pathology. In contrast, its low sensitivity for PTFL injuries limits its diagnostic reliability for this ligament.

Introduction: The Ottawa Ankle Rules (OAR) have demonstrated conflicting results across studies, with generally high sensitivity but relatively low and variable specificity. This updated meta-analysis aims to evaluate the overall diagnostic characteristics of the OAR for early identification of ankle fractures in adult patients.

Methods: In this systematic review and meta-analysis, a comprehensive literature search was conducted from database inception through May 2026 across three electronic databases, including Medline, Web of Science, and Scopus. The MIDAS package in STATA and Meta-Disc software were used to pool the findings of the diagnostic accuracy studies. Clinical application of the OAR was assessed using Fagan’s nomogram and scattergram.

Results: The pooled sensitivity and specificity of OAR were 0.92 (95% confidence interval (CI): 0.91–0.93) and 0.35 (95% CI: 0.34–0.36), respectively. The pooled positive likelihood ratio (PLR) and negative likelihood ratio (NLR) were 1.76 (95% CI: 1.46–2.13) and 0.13 (95% CI: 0.09–0.19), respectively. Furthermore, the pooled diagnostic odds ratio (DOR) was 16.21 (95% CI: 10.15–25.89).

Conclusion: In conclusion, the OAR demonstrate excellent sensitivity and a very low negative likelihood ratio, confirming their value as a reliable screening tool for ruling out ankle fractures in clinical practice. With pooled sensitivities exceeding 90% and a post-test probability reduced to approximately 1% following a negative result, the OAR can safely reduce unnecessary radiographic imaging.

A 69-year-old man presented with sudden-onset crushing chest pain, diaphoresis, and syncope with ECG findings prompting treatment as a ST-elevation myocardial infarction (STEMI). Emergency coronary angiography demonstrated no obstructive coronary artery disease. Subsequent investigations including cardiac magnetic resonance imaging (MRI) and positron emission tomography (PET) scan demonstrated an infiltrative mass. Biopsy of a supraclavicular lymph node established a diagnosis of diffuse large B-cell lymphoma (DLBCL) with cardiac involvement. The patient was treated with corticosteroids and immunochemotherapy (DA-R-EPOCH), achieving complete remission on surveillance imaging. This case therefore contributes valuable evidence regarding the varied cardiovascular manifestations of lymphoma and reinforces the importance of maintaining a broad differential diagnosis in patients presenting with features of myocardial infarction with non-obstructive coronary arteries (MINOCA).

Cardiac Advanced Life Support (CALS) differs from conventional Advanced Cardiac Life Support (ACLS) in utilizing targeted resuscitation protocols designed explicitly for post-cardiac surgery patients. The hallmark of CALS is the performance of prompt re-sternotomy and internal cardiac massage within 5 minutes of cardiac arrest if the patient is unresponsive to external chest compressions and rapid defibrillation. The standardized algorithms for ACLS, fundamental to managing cardiac arrest, present a significant and potentially dangerous dilemma when applied to patients who have undergone minimally invasive cardiac surgery (MICS). While MICS offers benefits like reduced trauma and faster recovery, it creates a unique physiological landscape that conflicts with conventional resuscitation. This letter highlights the urgent need to re-evaluate the ACLS protocol for this growing patient population. We advocate for the immediate development of a specialized MICS-specific resuscitation guideline that moves beyond a one-size-fits-all approach to in-hospital cardiac arrest.

Hantavirus infection is an uncommon but potentially life-threatening zoonotic disease that requires early recognition in the emergency department. This letter summarizes the epidemiology, transmission, clinical manifestations, diagnostic approaches, and current management of hantavirus pulmonary syndrome (HPS) and hemorrhagic fever with renal syndrome (HFRS). Although the initial presentation is often nonspecific, rapid progression to respiratory failure, shock, or acute kidney injury may occur, necessitating prompt diagnosis and aggressive supportive care. Serologic testing remains the primary diagnostic modality, while treatment is largely supportive, with extracorporeal membrane oxygenation reserved for selected patients with severe HPS. Increased awareness among emergency physicians and timely implementation of infection-control measures are essential to improve patient outcomes and reduce mortality.

The 2024 IHR amendments offer a practical roadmap for disaster and emergency health, translating clearer international alert triggers into faster local surge activation and embedding fairness into the allocation of vaccines, oxygen, and supplies for those most at risk. Strengthened national focal points and standardized data flows help bridge the stubborn gap between public health surveillance and frontline prehospital care, so Emergency Medical Teams can deploy with fewer delays. Digital tools like AI-driven forecasting can sharpen surge planning, but only when anchored in transparent, human-reviewed safeguards. Turning promise into real capability means testing new alert workflows through joint exercises, updating cross-border mutual aid agreements, and investing in field infrastructure alongside robust data governance. Ultimately, these amendments will be judged by whether they shorten the time from signal to response and narrow access gaps for the vulnerable—making future responses not just faster, but measurably fairer.

The rapid growth of diagnostic meta-analyses has highlighted concerns regarding heterogeneity assessment and statistical model selection. Although heterogeneity is commonly evaluated using the Cochrane Q test and I² statistic, conceptual and methodological judgment should guide model choice. Inadequate reporting of statistical models and reliance on software default settings can lead to discrepancies in pooled diagnostic estimates. Differences between Meta-DiSc and MIDAS are minimal under low heterogeneity but become pronounced with high heterogeneity, underscoring the need for transparent reporting and informed methodological decisions.