Early and accurate identification of intracranial hemorrhage (ICH) following traumatic brain injury (TBI) is crucial, yet conventional imaging techniques like CT have limitations including radiation exposure and overutilization. This study aimed to evaluate the diagnostic utility of two blood-based biomarkers, Glial Fibrillary Acidic Protein (GFAP) and Ubiquitin Carboxy-Terminal Hydrolase L1 (UCH-L1), in detecting ICH in emergency department patients with blunt head trauma. This single-center prospective observational study included 442 patients presenting with blunt head trauma at a university emergency department. Plasma GFAP and UCH-L1 levels were measured prior to cranial CT imaging. ROC curve analysis, sensitivity, specificity, and logistic regression were used to assess diagnostic performance and associations between biomarker levels and CT-confirmed ICH. ICH was detected in 6.1% of patients. GFAP and UCH-L1 both demonstrated high diagnostic performance with AUC values of 0.98 and 0.95, respectively. Sensitivity and specificity were 92.6% and 98.8% for GFAP, and 92.6% and 99.0% for UCH-L1. When both biomarkers were concurrently elevated, sensitivity remained at 92.6% with a negative predictive value of 99.5%. Univariate logistic regression revealed strong associations with ICH: GFAP (OR = 1025.0, 95% CI: 189.4–5548.3) and UCH-L1 (OR = 1284.4, 95% CI: 224.4–7352.3), both p < 0.001. GFAP and UCH-L1 are highly sensitive and specific biomarkers for early ICH detection in blunt head trauma. Their combined use may enhance triage accuracy, reduce unnecessary imaging, and support rapid clinical decision-making in emergency settings. Larger multicenter studies are warranted to validate these findings and standardize cut-off values.

Crimean-Congo hemorrhagic fever (CCHF) is a potentially fatal tick-borne viral disease that remains an important public health problem in endemic regions.

Background/Objectives: Ambulance-transported patients represent an important subgroup of emergency department (ED) admissions and may demonstrate distinct temporal presentation patterns. However, studies simultaneously evaluating broad diagnostic categories according to both time-of-day and seasonal variations among ambulance-transported patients remain limited. Methods: This retrospective observational study included 37,188 patients transported to the emergency department of a tertiary university hospital by ambulance between December 1, 2024, and November 30, 2025. Patients were categorized into twelve major diagnostic groups according to ICD-10-based clinical diagnoses. Temporal analyses were performed according to seasons and time-of-day intervals (nighttime, daytime, and evening). Associations between diagnostic categories and temporal variables were evaluated using chi-square analysis. Results: Trauma was the most common diagnostic category (19.9%), followed by cardiovascular (16.3%), gastrointestinal (10.3%), and respiratory diseases (10.1%). Significant associations were observed between diagnostic categories and both seasonal distribution (χ² = 446.482, df = 33, p < 0.001) and time of presentation (χ² = 642.883, df = 22, p < 0.001). Respiratory diseases were most frequent during winter (32.7%), whereas trauma-related admissions peaked during summer (32.4%). Psychiatric presentations predominantly occurred during evening hours (53.2%), while gastrointestinal diseases demonstrated the highest proportion of nighttime admissions (24.2%). Trauma-related admissions were more common among males (63.6%), whereas neurological, gastrointestinal, metabolic, and psychiatric presentations were slightly more frequent among females. The mean age of the cohort was 49.02 ± 25.12 years. Conclusions: Ambulance-transported emergency department patients demonstrate significant seasonal and time-of-day variations across major diagnostic categories. Awareness of these temporal patterns may contribute to improved emergency department organization, ambulance deployment, staffing strategies, and healthcare resource management.

Objective: To evaluate seasonal and time-of-day variations in station-to-scene ambulance travel times and assess potential interaction effects between these temporal factors among trauma patients attended by emergency medical services (EMS) in Erzincan, Türkiye. Methods: This retrospective observational study analyzed regional database records of trauma-related EMS activations resulting in ambulance transport between December 1, 2024, and November 30, 2025. Ambulance travel time was defined as the exact interval (in seconds) between station departure and scene arrival. Travel times were compared across 4 seasons and 3 operational shifts (nighttime, daytime, and evening) using 1-way and 2-way analysis of variance. Results: A total of 7417 trauma-related EMS activations were analyzed. The mean (SD) age of the cohort was 39.44 (24.03) years, 63.6% (n = 4715) involved male patients, and the overall mean (SD) ambulance travel time was 515.80 (648.60) seconds. Travel times did not exhibit statistically significant variations across seasons (P = .229). Conversely, a statistically significant difference was observed based on the time of day (P = .045), with the longest intervals occurring during the evening shift (536.07 seconds) and the shortest during the daytime shift (496.03 seconds). Post hoc analysis identified a significant disparity exclusively between daytime and evening shifts, with a mean difference of approximately 40.04 seconds (P = .034). No significant interaction was found between season and time of day (P = .368). Conclusions: In this regional dataset, station-to-scene ambulance travel times for trauma activations demonstrated stable seasonal performance but minor time-of-day fluctuations, with slightly prolonged travel times during evening shifts. Future prospective multicenter studies integrating traffic density indexes and clinical outcomes are warranted.

Investigation of Sensitivity and Specificity of Biochemical Markers for Cardiac Contusion due to Blunt Chest Trauma - Archives of Basic and Clinical Research

Background: Accurate and timely diagnosis in emergency departments (EDs) is critical due to the high patient volume and time-sensitive nature of care. Intern doctors (IDs), who are about to graduate from medical school, often work in EDs for a period in many countries. However, after graduate, physicians are often expected to take on critical patient care responsibilities despite limited experience. Artificial intelligence (AI) models can rapidly analyze patient data and generate diagnoses, thereby assisting inexperienced physicians in improving diagnostic accuracy. This study aims to evaluate the diagnostic performance of ChatGPT-4 in ED case scenarios and compare its accuracy with that of IDs. Methods: This study was conducted with IDs participating in the internship program during the 2024 academic term. A total of 36 case-based questions, categorized by difficulty level. These questions were administered to 155 IDs via Google Documents and subsequently presented to AI. Descriptive statistics were used to summarize the data, and a one-sample t-test was performed to compare diagnostic accuracy between IDs and ChatGPT. Statistical significance was set at p < 0.05. Results: IDs achieved an overall correct response rate of 58.3%, while ChatGPT reached a rate of 97.2%. A statistically significant, moderate negative correlation was observed between question difficulty and IDs’ performance (r = -0.684; p < 0.001), indicating decreased accuracy as question difficulty increased. ChatGPT consistently maintained significantly higher performance regardless of difficulty level. Conclusion: ChatGPT-4 may serve as a valuable diagnostic support tool in EDs. It can be particularly beneficial for newly graduated physicians with limited clinical experience. Clinical trial number: not applicable .