Increased suicidal activity following major trauma
Bibliographic record
Abstract
BACKGROUND: Nonfatal injuries are a leading cause of morbidity and mortality. In 2008, 14,065 patients with major trauma were hospitalized across Canada. With individuals surviving trauma, the psychosocial sequelae of severe physical injury have become an important area of research. No previous studies have used a population-based sample to estimate the incidence of suicidality (suicide or suicide attempt) following physical injury. This study aimed to assess the odds ratio (OR) of suicidality among adults with major trauma compared with a matched cohort. METHODS: This retrospective study included persons older than 18 years who experienced an unintentional major traumatic injury (Injury Severity Score [ISS] > 12) at a regional academic trauma center between April 1, 2001, and March 31, 2011. Individuals who had no suicide attempts in the previous 5 years were identified from the trauma registry. These individuals were matched with data from provincial administrative databases. A cohort matched in terms of age, sex, and date of indexed injury was created from the general population with five controls for each trauma case, and the rate of suicidality was compared between groups. RESULTS: A total of 2,198 adults with major were matched to 10,990 individuals. Suicidality was increased in the trauma cohort (OR, 4.31). This increase persisted even if adjusted for anxiety/mood disorders and substance abuse (adjusted OR1, 3.65) as well as residence, physical comorbidities, income quintile and those factors in adjusted OR1 (adjusted OR2, 3.30). All ORs were significant with p < 0.05 CONCLUSION: Individuals who experience major traumatic injuries are at a greater risk for postinjury suicidality compared with those in a matched cohort. LEVEL OF EVIDENCE: Epidemiologic study, level IV.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".