Archives of Academic Emergency Medicine

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: 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: 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: 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.

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: 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: 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: 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: 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: 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: 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: 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: 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: 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: 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.

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.

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: 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: 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.

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: 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.

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.