Substances Used in Completed Suicide by Overdose in Toronto: An Observational Study of Coroner's Data
Bibliographic record
Abstract
OBJECTIVE: To identify the substances used by people who die from suicide by overdose in Toronto and to determine the correlates of specific categories of substances used. METHOD: Coroner's records for all cases of suicide by overdose in Toronto, Ontario, during a 10-year period (1998 to 2007) were examined. Data collected included demographic data, all substances detected, and those determined by the coroner to have caused death. Logistic regression analyses were used to examine demographic and clinical factors associated with suicide by different drug types. RESULTS: There were 397 documented suicides by overdose (mean age 49.1 years, 50% female). Most substances detected were psychotropic prescription medications (n = 245), followed by other prescription medications (n = 143) and over-the-counter (OTC) medications (n = 83). More than one-half of all suicides by overdose were determined to have only one specific substance as the cause of death (n = 206). In suicides where only one class of substance was present in lethal amounts, OTC medication (n = 48), opioid analgesics (n = 44), and tricyclic antidepressants (n = 44) were most common. CONCLUSIONS: Suicides by overdose involved the use of different classes of substances, including psychotropic prescription medication, other prescription medications, as well as OTC medications. Physicians and pharmacists should be aware of commonly used prescription and OTC medications in overdose and exercise increased vigilance in prescribing or dispensing them to at-risk patients.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".