Clinician Prediction of Future Suicide Attempts
Bibliographic record
Abstract
Objective: Established risk assessment tools are often inaccurate at predicting future suicide risk. We therefore investigated whether clinicians are able to predict individuals’ suicide risk with greater accuracy. Method: We used the SAFE Database, which included consecutive adult (age ≥18 years) presentations ( N = 3818) over a 22-month period to the 2 tertiary care hospitals in Manitoba, Canada. Medical professionals assessed each individual and recorded his or her predicted risk for future suicide attempt (SA) on a 0-10 scale—the clinician prediction scale. The SAD PERSONS scale was completed as a comparison. SAs within 6 months, assessed using the Columbia Classification Algorithm for Suicide Assessment, were the primary outcome measure. Receiver operating characteristic curve and logistic regression analyses were conducted to determine the accuracy of both scales to predict SAs, and the scales were compared with z scores. Clinician prediction scale performance was stratified based on level of training. Results: Clinicians were able to predict future SAs with significantly greater accuracy (area under the curve [AUC] = 0.73; 95% CI, 0.68 to 0.77; P < 0.001) compared with the SAD PERSONS scale ( z = 3.79, P < 0.001). Both scales nonetheless showed positive predictive value of less than 7%. Analyses by level of training showed that junior psychiatric residents and non–psychiatric residents did not accurately predict SAs, whereas senior psychiatric residents and staff psychiatrists demonstrated greater accuracy (AUC = 0.76 and 0.78, respectively). Conclusions: Clinicians are able to predict future attempts with fewer false positives than a conventional risk assessment scale, and this skill appears related to training level. Predicting future suicidal behaviour remains very challenging.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".