Trauma Recidivism in a Large Urban Canadian Population
Bibliographic record
Abstract
BACKGROUND: Prevention of trauma might be achieved by risk factor modification. Identification of such risk factors can be pursued by various means. Trauma recidivists may possess and highlight risk factors. Accordingly, trauma recidivists were analyzed as a method to elucidate trauma risk factors. METHODS: A retrospective analysis of 13,057 trauma patients in Toronto was conducted. Forty-two recidivists were identified, and their first admission was compared with a control group of 84 non-recidivists. RESULTS: The rate of trauma recidivism was 0.38% overall. Trauma recidivists were more likely to be from the inner city, male, homeless, suffering from chronic medical conditions. In addition, psychiatric conditions, an alcoholism history or any alcohol at the time of injury, intentionally injured, or engaged in criminal activity were also significantly more common in recidivists (p <0.05). CONCLUSION: Risk factors for major trauma can be identified by analyzing recidivists in a large urban Canadian population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".