Enhancing patient safety in trauma: Understanding adverse events, assessment tools, and the role of trauma video review
Bibliographic record
Abstract
OBJECTIVES: This study aimed to investigate adverse events (AEs) in trauma resuscitation, evaluate contributing factors, and assess methods, such as trauma video review (TVR), to mitigate AEs. BACKGROUND: Trauma remains a leading cause of global mortality and morbidity, necessitating effective trauma care. Despite progress, AEs during trauma resuscitation persist, impacting patient outcomes and the healthcare system. Identifying and analyzing AEs and their determinants are crucial for improving trauma care. METHODS: This narrative review explored the definition, identification, and assessment of AEs associated with trauma resuscitation within the trauma system. It includes various studies and assessment tools such as STAT Taxonomy and T-NOTECHs. Additionally, it assessed the role of TVR in detecting AEs and strategies to enhance patient safety. CONCLUSION: Integrated with standardized tools, TVR shows promise for identifying AEs. Challenges include ensuring reporting consistency and integrating approaches into existing protocols. Future research should prioritize linking trauma team performance to patient outcomes, and develop sustainable TVR programs to enhance patient safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.048 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".