Predicting suicide with the SAD PERSONS scale
Bibliographic record
Abstract
BACKGROUND: Suicide is a major public health issue, and a priority requirement is accurately identifying high-risk individuals. The SAD PERSONS suicide risk assessment scale is widely implemented in clinical settings despite limited supporting evidence. This article aims to determine the ability of the SAD PERSONS scale (SPS) to predict future suicide in the emergency department. METHODS: Five thousand four hundred sixty-two consecutive adults were seen by psychiatry consultation teams in two tertiary emergency departments with linkage to population-based administrative data to determine suicide deaths within 6 months, 1, and 5 years. RESULTS: Seventy-seven (1.4%) individuals died by suicide during the study period. When predicting suicide at 12 months, medium- and high-risk scores on SPS had a sensitivity of 49% and a specificity of 60%; the positive and negative predictive values were 0.9 and 99%, respectively. Half of the suicides at both 6- and 12-month intervals were classified as low risk by SPS at index visit. The area under the curve at 12 months for the Modified SPS was 0.59 (95% confidence interval [CI] range 0.51-0.67). High-risk scores (compared to low risk) were significantly associated with death by suicide over the 5-year study period using the SPS (hazard ratio 2.49; 95% CI 1.34-4.61) and modified version (hazard ratio 2.29; 95% CI 1.24-2.29). CONCLUSIONS: Although widely used in educational and clinical settings, these findings do not support the use of the SPS and Modified SPS to predict suicide in adults seen by psychiatric services 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".