Mental health outcomes after major trauma in Ontario: a population-based analysis
Bibliographic record
Abstract
BACKGROUND: Major injury continues to be a common source of morbidity and mortality; improving the functional recovery of survivors of major trauma requires a better understanding of the mental health outcomes that may occur in this population. We assessed the association between major trauma and the development of a new mental health diagnosis or death by suicide. METHODS: We completed a population-based, self-controlled, longitudinal cohort analysis using linked administrative data on patients treated for major trauma in Ontario between 2005 and 2010. All survivors were included and composite rates of mental health diagnoses during inpatient admissions were compared between the 5 years after injury and the 5 years before injury, using Poisson regression with generalized estimating equations. The incidence of suicide was calculated for the 5 years after injury. Risk factors for suicide were calculated using Cox proportional hazard regression analyses. RESULTS: The analysis included 19 338 patients, predominantly men (70.7%) from urban areas (82.6%), with unintentional (89%), blunt injuries (93.4%). Overall, trauma was associated with a 40% increase in the postinjury rate of mental health diagnoses (incidence rate ratio [IRR] 1.4, 95% [confidence interval] CI 1.1 to 1.8). The suicide rate was 70 per 100 000 patients per year, substantially higher than the population average. Risk factors for completing suicide were prior inpatient diagnosis of mood disorder (hazard ratio [HR] 4.3, 95% CI 2.1 to 8.8) and self-inflicted injury (HR 7.8, 95% CI 3.9 to 15.4). INTERPRETATION: Survivors of major trauma are at a heightened risk of developing mental health conditions or death by suicide in the years after their injury. Patients with pre-existing mental health disorders or who are recovering from a self-inflicted injury are at particularly high risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".