Crew Resource Management in the trauma room: a prospective 3-year cohort study
Bibliographic record
Abstract
OBJECTIVE: Human factors account for the majority of adverse events. Human factors awareness training entitled Crew Resource Management (CRM) is associated with improved safety and reduced complications and mortality in critically ill patients. We determined the effects of CRM implementation in the trauma room of an Emergency Department (ED). PATIENTS AND METHODS: A prospective 3-year cohort study was carried out in a level 1 ED, admitting more than 12 000 patients annually (>1500 trauma related). At the end of the baseline year, CRM training was performed, followed by an implementation year. The third year was defined as the clinical effect year. The primary outcomes were safety climate, measured using the Safety Attitudes Questionnaire, and ED length of stay. The secondary outcome measures were hospital length of stay and 48-h crude mortality of trauma patients. RESULTS: All 5070 trauma patients admitted to the ED during the study period were included. Following CRM implementation, safety climate improved significantly in three out of six Safety Attitudes Questionnaire domains, both at the end of the implementation and clinical effect years: teamwork climate, safety climate, and stress recognition. ED length of stay of these patients increased from 141 (102-192) in the baseline year to 161 (116-211) and 170 (128-223) min in the implementation and clinical effect years, respectively (P<0.05 vs. baseline). Hospital length of stay was prolonged by 1 day in the implementation and clinical effect years (P<0.05 vs. baseline), whereas mortality was unaltered. CONCLUSION: Although CRM implementation in the ED was associated with an improved safety climate, the time spent by trauma patients in the ED increased.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".