Missed Injuries in Patients with Multiple Trauma
Bibliographic record
Abstract
BACKGROUND: Understanding the etiology of missed injuries is essential in minimizing its occurrence. A retrospective review was conducted to identify the incidence, contributing factors, and clinical outcomes of missed injuries. METHODS: All trauma patients assessed by St Michael's Hospital trauma service from April 1, 1995, to July 31, 1997, were included in the study. Demographic and medical data were compared and statistically analyzed in two patient groups to identify factors associated with missed injuries. RESULTS: Forty six of 567 patients (8.1%) had missed injuries. Patients with missed injuries had higher mean Injury Severity Scores and longer stays in the hospital and intensive care unit compared with patients without missed injuries (p < 0.05). Patients with missed injuries were more likely to have lower Glasgow Coma Scale scores and to have required pharmacologic paralysis (p < 0.05). Of the factors contributing to missed injuries, 56.3% were potentially avoidable and 43.8% were unavoidable. Seven patients with missed injuries had clinically significant outcomes, including one patient death. Of the seven clinically significant missed injuries, five were attributable to potentially avoidable factors. CONCLUSION: Patients with missed injuries tend to be more severely injured with initial neurologic compromise. The majority of missed injuries are potentially avoidable with repeat clinical assessments and a high index of suspicion.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".