Burns and Mental Health
Bibliographic record
Abstract
OBJECTIVE: To determine the association between burns and hospitalization for mental health disorders up to 3 decades later. BACKGROUND: Burns are associated with pain, disability, and scarring, but the long-term impact on mental health is unclear. METHODS: We analyzed a cohort of 23,726 patients with burns aged ≥10 years who were matched to 223,626 controls from Quebec, Canada, between 1989 and 2022. The main exposure was admission for a burn. We followed patients during 3,642,206 person-years of follow-up to identify future hospitalizations for psychiatric disorders, substance use disorders, and suicide attempts. We estimated adjusted hazard ratios (HRs) with 95% CIs for the association between burns and subsequent mental health hospitalization using Cox proportional hazards regression. RESULTS: Patients with burns had a 1.76 times greater risk of mental health hospitalization over time (95% CI: 1.72-1.81), compared with controls. Associations were present regardless of burn site, but were greatest for burns covering ≥50% of the body (HR: 3.29, 95% CI: 2.61-4.15), third-degree burns (HR: 2.04, 95% CI: 1.94-2.14), and burns requiring skin grafts (HR: 2.00, 95% CI: 1.90-2.10). Compared with controls, patients with burns had more than two times the risk of hospitalization for eating disorders (HR: 3.14, 95% CI: 2.50-3.95), psychoactive substance use disorders (HR: 2.27, 95% CI: 2.17-2.39), and suicide attempts (HR: 2.42, 95% CI: 2.23-2.62). Risks were particularly elevated within 5 years of the burn but persisted throughout follow-up. CONCLUSIONS: Burns are associated with an increased risk of hospitalization for mental health disorders up to 30 years later.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".