Suicide in bipolar disorder: characteristics and subgroups
Bibliographic record
Abstract
OBJECTIVES: The development of more sophisticated models for understanding suicide among people with bipolar disorder (BD) requires diagnosis-specific data. The present study aimed to elucidate differences between people who die by suicide with and without BD, and to identify subgroups within those with BD. METHODS: Data on all suicide deaths in the city of Toronto from 1998 to 2010 were extracted from the Office of the Chief Coroner of Ontario, including demographics, clinical variables, recent stressors, and details of the suicide. Comparisons of person- and suicide-specific variables between suicide deaths among those with BD (n = 170) and those without (n = 2,716) were conducted, and a cluster analysis was performed among the BD suicide group only. RESULTS: Those in the BD suicide group were more likely than those in the non-BD suicide group to be female [odds ratio (OR) = 1.75, 95% confidence interval (CI): 1.27-2.42; p = 0.001], to have made a past suicide attempt (OR = 2.01, 95% CI: 1.45-2.80; p < 0.0001), and to have had recent contact with psychiatric or emergency services (OR = 1.59, 95% CI: 1.00-2.52; p = 0.049). Five clusters were identified within the BD group, with differences between clusters in age; sex; marital status; living circumstances; past suicide attempts; substance abuse; interpersonal, employment/financial, and legal/police stressors; and rates of death by fall/jump or self-poisoning. CONCLUSIONS: The present findings identified differences between BD and non-BD suicide groups, providing support to the utilization of an illness-specific approach to better understanding suicide in BD. Subgroups of BD suicide deaths, if replicated, should also be incorporated into the design and analysis of future studies of suicide in BD.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".