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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".