The demographics of significant firearm injury in Canadian trauma centres and the associated predictors of inhospital mortality.
Bibliographic record
Abstract
OBJECTIVE: Our primary objective was to evaluate demographic and causal factors of inhospital mortality for significant firearm-related injuries (i.e., those with an Injury Severity Score [ISS] > 12) in Canadian trauma centres. METHODS: We analyzed data submitted to the Canadian Institute for Health Information (CIHI) in the National Trauma Registry for all firearm-injured patients for fiscal years 1999-2003. Univariate and bivariate adjusting for ISS and multivariate logistic regression were performed. RESULTS: Men accounted for 94% of the 784 injured. In all patients, the percentages of self-inflicted, intentional, unintentional and unknown injuries were 27.8%, 60.3%, 6.1% and 5.7%, respectively. The inhospital fatality rate was 39.8%, with 83% of fatalities occurring on the first day. Two-thirds of patients were discharged home. Univariate and adjusted analysis found that ISS, first systolic blood pressure (BP), first systolic BP under 100, first Glasgow Coma Scale (GCS) score, age over 45 years, self-inflicted injury, intentional injury and injury at home significantly worsened the odds ratio of death in hospital and that police shooting was relatively beneficial. BP under 100, age over 45 years and a low GCS score had an adjusted odds ratio of death of 4.12, 1.99 and 0.64 per point increase, respectively. The multivariate model showed that ISS, BP under 100, first GCS score, sex and self-inflicted injury were significant in predicting inhospital death. CONCLUSION: A predominance of young men are injured intentionally with handguns in Canada, whereas older patients suffer self-inflicted injuries with long guns. The significant number of firearm deaths, largely in the first day, highlights the importance of preventative strategies and the need for rapid transport of patients to trauma centres for urgent care.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".