Traumatic brain injury (TBI) remains a major global public health problem. This study aimed to evaluate the association between pre-existing bleeding disorders and clinical outcomes among patients with TBI. We hypothesized that pre-existing bleeding disorders would be associated with worse outcomes following TBI. We conducted a retrospective cohort study using the 2021–2023 National Inpatient Sample (NIS) to evaluate the association between pre-existing bleeding disorders and clinical outcomes among patients aged 15 years or older hospitalized with TBI. Mortality was significantly higher in the bleeding disorder group (13.9
Conventional trauma prognostic models rely primarily on admission data and cannot account for subsequent physiological response to treatment. We evaluated whether routinely collected physiological information available at 24 h provides incremental prognostic information for major intensive care unit (ICU) complications in severely injured patients. We conducted a prospective cohort of adults with severe trauma (Injury Severity Score [ISS] ≥ 15) admitted to the ICU between January 2024 and April 2025. The primary outcome was a composite of major respiratory, hemodynamic, renal, or septic complications during the ICU course. TRISS was compared with an admission ridge model incorporating ISS, Glasgow Coma Scale, hemoglobin, lactate, and international normalized ratio, and a 24-h updated ridge model additionally incorporating 24-h lactate and change in creatinine. Performance was internally validated with 1,000 bootstrap resamples. Among 109 patients, median ISS was 29 (IQR 20–38), and 73 (67.0
There is a high demand within our society for resilient, sustainable and efficient trauma and orthpedic surgery (TOS). The shortage of young professionals threatens the provision of comprehensive care in TOS. Alongside factors, such as payment, working hours and work-life balance, the perceived attractiveness of the role is a key determinant of personal job satisfaction and fulfilment. The relationship between the perception of one's own role, the associated variety of tasks and the resulting job satisfaction, has scarcely been investigated among specialists in TOS. This study examines the self-perception and external perception of the role as well as the attributed job satisfaction, in relation to clinical practice and the stage of specialist training. The study was based, amongst other things, on Hackman and Oldham's 5-dimensional job characteristics theory. A quantitative online survey was conducted among 8946 members of the German Society for Orthopaedic and Trauma Surgery (DGOU). The results show that job satisfaction is lowest amongst those at the start of their careers. The group comprising junior doctors and specialists describe task variety, the holistic nature of the role and feedback as important for their own satisfaction. At the same time, doctors and specialists in training associate career progression with an increase in scores across all five dimensions. In contrast, senior doctors attribute higher scores to the tasks of doctors and specialists in further training across all dimensions than those perceived by the trainees themselves. Feedback and interaction are crucial for how they interact and communicate with one another. The group of doctors and specialists in training regard feedback, diversity of tasks, the holistic nature of the role and autonomy as important and a lack of feedback as demotivating. Action is required, amongst other things, to strengthen mutual understanding in line with the activities perceived in the workplace. Key tools are communication and feedback. The authors identify shortcomings and propose solutions in this article.
The care of severely injured patients within a trauma network requires cross-sectoral coordination from the prehospital setting through to rehabilitation. Digital applications, such as teleconsultations, real-time situational mapping and artificial intelligence (AI)-assisted decision support have the potential to accelerate information flow, enable more targeted use of resources and improve the quality of care. An analysis of the TKmed (R) transfer statistics at the University Medical Center G & ouml;ttingen demonstrated a continuous increase in import and export volumes between 2015 and 2025. Teleconsultations enable the early involvement of specialist expertise and can support triage, treatment and transfer decisions while reducing unnecessary secondary transfers. The interdisciplinary care capacity proof system (IVENA) provides a shared real-time overview of available capacities and improves coordination and patient allocation, particularly during mass casualty incidents. Artificial intelligence (AI)-assisted triage systems can facilitate the earlier identification of critical clinical deterioration and reduce both overtriage and undertriage. In radiology AI systems can support the prioritization of abnormal findings and accelerate diagnostic processes. Prerequisites for a safe implementation are stable infrastructures, standardized interfaces, validated algorithms and clearly defined responsibilities. Digital systems should complement and not replace clinical decision-making.
Background Generative artificial intelligence (AI) and large language models (LLM) are evolving from general purpose text assistants toward increasingly contextualized, multimodal systems integrated into clinical workflows. Objective Presentation of current applications of generative AI and LLMs in trauma surgery, with a focus on clinical care and additional aspects of research and teaching. Material and methods Narrative literature review based on a MEDLINE/PubMed search and supplementary hand search, including primary regulatory sources. Results The most immediate clinical potential currently lies in documentation, information processing and patient communication. Initial studies demonstrate relevant time savings with AI-assisted documentation. Current systems achieve high performance in some clearly defined triage and decision-support tasks, whereas complex individualized and multimodal applications show lower and more heterogeneous reliability. In research and teaching LLMs can support standardized workflows, case generation and learning activities; however, the available evidence is still frequently based on retrospective or simulated studies. Conclusion The safe implementation requires validation of the specific AI system in its intended context of use, reliable knowledge sources, sufficient clinical context, data protection and effective human control. As the clinical consequences of AI-generated outputs increase, so do the requirements for validation and physician responsibility in making decisions.
