The role of trauma team leaders in missed injuries
Bibliographic record
Abstract
BACKGROUND: Previous studies have identified missed injuries as a common and potentially preventable occurrence in trauma care. Several patient- and injury-related variables have been identified, which predict for missed injuries; however, differences in rate and severity of missed injuries between surgeon and nonsurgeon trauma team leaders (TTLs) have not previously been reported. METHODS: A retrospective review was conducted on a random sample of 10% of all trauma patients (Injury Severity Score [ISS] > 12) from 1999 to 2009 at a Canadian Level I trauma center. Missed injuries were defined as those identified greater than 24 hours after presentation and were independently adjudicated by two reviewers. TTLs were identified as either surgeons or nonsurgeons. RESULTS: Of our total trauma population of 2,956 patients, 300 charts were randomly pulled for detailed review. Missed injuries occurred in 46 patients (15%). Most common missed injuries were fractures (n = 32, 70%) and thoracic injuries (n = 23, 50%). The majority of missed injuries resulted in minor morbidity with only 5 (11%) requiring operative intervention. On univariate analysis, higher ISS (p < 0.01), higher maximum Abbreviated Injury Scale (MAIS) score of the thorax (p < 0.01), and nonsurgeon TTL status were predictive of missed injuries (p = 0.02). Multivariable logistic regression revealed that, after adjustment for age, ISS, and severe head injuries, the presence of a nonsurgeon TTL was associated with an increased odds of missed injury (odds ratio, 2.15; 95% confidence interval, 1.10-4.20). CONCLUSION: Missed injuries occurred in 15% of patients. A unique finding was the increased odds of missed injury with nonsurgeon TTLs. Further research should be undertaken to explore this relationship, elucidate potential causes, and propose interventions to narrow this discrepancy between TTL provider types. LEVEL OF EVIDENCE: Therapeutic study, level IV. Prognostic and epidemiologic study, level III.
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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 | 0.004 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".