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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".