Death and long-term disability after gun injury: a cohort analysis
Bibliographic record
Abstract
Background: Gun injury accounts for substantial acute mortality worldwide and many others survive with lingering disabilities. We investigated whether additional health losses beyond mortality can also arise for patients who survive with long-term disability. Methods: We conducted a population-based individual patient analysis of adults injured by firearms who had received emergency medical care in Ontario, Canada, from Apr. 1, 2002, to Apr. 1, 2019. Longitudinal cohort analyses were evaluated through deterministic linkages of individual electronic patient files. The primary outcome was death or subsequent application for long-term disability in the years after hospital discharge. Results: In total, 8313 patients were injured from firearms, of which 3020 were injured from intentional incidents and 5293 were injured from unintentional incidents. A total of 2657 (88.0%) patients with intentional gun injury and 5089 (96.1%) patients with unintentional gun injury survived initial injuries. After a mean 7.75 years of follow-up, patients surviving intentional injuries had a disability rate twice as high as patients surviving unintentional injuries (19.7% v. 10.1%, p < 0.001), equivalent to a hazard ratio of 2.01 (95% confidence interval 1.80–2.25). The higher risk of long-term disability for survivors after intentional gun injury was not explained by demographic characteristics, extended to survivors treated and released from the emergency department, and was observed regardless of whether the incident was self-inflicted or from interpersonal assault. Half of the disability cases were identified after the first year. Additional predictors of long-term disability included a lower socioeconomic status, an urban home location, arrival by ambulance transport, a history of mental illness and a diagnosis of substance use disorder. Interpretation: Our study shows that gun death statistics underestimate the extent of health losses from long-term disability, particularly for those with intentional injuries. Additional and sustainable follow-up medical care might improve patient outcomes.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".