Predicting Future Suicide: Clinician Opinion versus a Standardized Assessment Tool
Bibliographic record
Abstract
OBJECTIVE: To compare the effectiveness of clinician prediction of risk to a standardized assessment of presentation status. METHODS: All adult psychiatry emergency department consults in the two main hospitals in Winnipeg, Canada, were assessed using a standardized form (n = 5,376). This form includes two risk scales for a gestalt physician assessment of risk (Suicide Likelihood scale, suicide Attempt Likelihood scale) and the Columbia Classification Algorithm of Suicide Assessment (C-CASA). Regression determined whether assessments predicted future suicide attempts and deaths. The area under the curve (AUC) determined the prediction accuracy of these methods. RESULTS: Although the regression results were significant, the AUCs were either moderate or poor. Clinician assessment was not effective at predicting deaths (AUC = .546, .36-.73), but moderately accurate at predicting future attempts (AUC = .728, .66-.79). C-CASA assessment was moderately accurate at predicting both attempts and deaths (AUC = .666 and .678). CONCLUSIONS: Clinician assessment does not significantly outperform a simple assessment of the occurrence of suicidal thoughts and behaviors during presentation to the emergency department. Behavior-based standardized assessments should be further researched in this field. Assessment of suicidality at presentation using C-CASA or similar assessment should be standard for psychiatric patients assessed in the emergency department.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".