Re-Examination of Classic Risk Factors for Suicidal Behavior in the Psychiatric Population
Bibliographic record
Abstract
BACKGROUND: For decades we have understood the risk factors for suicide in the general population but have fallen short in understanding what distinguishes the risk for suicide among patients with serious psychiatric conditions. AIMS: This prompted us to investigate risk factors for suicidal behavior among psychiatric inpatients. METHOD: We reviewed all psychiatric hospital admissions (2008-2011) to a centralized psychiatric hospital in Ontario, Canada. Using multivariable logistic regression we evaluated the association between potential risk factors and lifetime history of suicidal behavior, and constructed a model and clinical risk score to predict a history of this behavior. RESULTS: The final risk prediction model for suicidal behavior among psychiatric patients (n = 2,597) included age (in three categories: 60-69 [OR = 0.74, 95% CI = 0.73-0.76], 70-79 [OR = 0.45, 95% CI = 0.44-0.46], 80+ [OR = 0.31, 95% CI = 0.30-.31]), substance use disorder (OR = 1.30, 95% CI = 1.27-1.32), mood disorder (OR = 1.49, 95% CI = 1.47-1.52), personality disorder (OR = 2.30, 95% CI = 2.25-2.36), psychiatric disorders due to general medical condition (OR = 0.52, 95% CI = 0.50-0.55), and schizophrenia (OR = 0.42, 95% CI = 0.41-0.43). The risk score constructed from the risk prediction model ranges from -9 (lowest risk, 0% predicted probability of suicidal behavior) to +5 (highest risk, 97% predicted probability). CONCLUSION: Risk estimation may help guide intensive screening and treatment efforts of psychiatric patients with high risk of suicidal behavior.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".