Repetition of intentional drug overdose: a population-based study
Bibliographic record
Abstract
CONTEXT: Intentional overdose is a leading method of self-harm and suicide, and repeat attempts strongly predict eventual death by suicide. OBJECTIVES: To determine the risk of recurrence after a first intentional overdose. Secondary objectives included characterization of the temporal course and potential predictors of repeat overdose, a strong risk factor for death from suicide. DESIGN: Population-based cohort study. SETTING: Ontario, Canada, from 1 April 2002 to 31 March 2013. PARTICIPANTS: All Ontario residents presenting to an emergency department after a first intentional overdose. MAIN OUTCOME MEASURES: The incidence and timing of recurrent overdose. RESULTS: We followed 81,675 patients discharged from hospital after a first intentional overdose. Overall, 13,903 (17.0%) returned with a repeat overdose after a median interval of 288 (inter-quartile range: 62 to 834) days. Of these, 4493 (5.5%) had multiple repeat episodes. Factors associated with repeat self-poisoning included psychiatric care in the preceding year (adjusted hazard ratio [aHR] 1.55; 95% confidence interval [CI] 1.50 to 1.61), alcohol dependence (aHR 1.41; 95% CI 1.35 to 1.46) and documented depression (aHR 1.39; 95% CI 1.34 to 1.44). Female sex, rural residence, lower socioeconomic status, ingestion of psychoactive drugs and younger age were also weakly associated with repeat overdose. DISCUSSION: Hospital presentation for repetition of intentional overdose is common, with recurrent episodes often far removed from the first. While several factors predict overdose repetition, none is particularly strong. CONCLUSION: Secondary prevention initiatives should be implemented for all individuals who present to the emergency department and survive intentional overdose.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| 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".