Self-poisoning suicide deaths in people with bipolar disorder: characterizing a subgroup and identifying treatment patterns
Bibliographic record
Abstract
OBJECTIVE: To characterize self-poisoning suicide deaths in BD compared to other suicide decedents. METHODS: Extracted coroner data from all suicide deaths (n = 3319) in Toronto, Canada from 1998 to 2012. Analyses of demographics, clinical history, recent stressors, and suicide details were conducted in 5 subgroups of suicide decedents: BD self-poisoning, BD other methods, non-BD self-poisoning, non-BD other methods, and unipolar depression self-poisoning. Toxicology results for lethal and present substances were also compared between BD and non-BD self-poisoning subgroups as well as between BD and unipolar depression self-poisoning subgroups. RESULTS: Among BD suicide decedents, self-poisoning was significantly associated with female sex, past suicide attempts, and comorbid substance abuse. In both the BD and non-BD self-poisoning groups, opioids were the most common class of lethal medication. For both groups, benzodiazepines and antidepressants were the most common medications present at time of death, and in 23% of the BD group, an antidepressant was present without a mood stabilizer or antipsychotic. Only 31% of the BD group had any mood stabilizer present, with carbamazepine being most common. No antidepressant, mood stabilizer, or antipsychotic was present in 15.5% of the BD group. Relative to unipolar depression self-poisoning group, the BD self-poisoning group evidenced higher proportion of previous suicide attempt(s) and psychiatry/ER visits in the previous week. CONCLUSION: People with BD who die by suicide via self-poisoning comprise a distinct but understudied group. The predominant absence of guideline-concordant pharmacologic care comprises a crucial target for future policy and knowledge translation efforts.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".