The Suicide Risk Assessment and Management Manual (S-RAMM) Validation Study II
Bibliographic record
Abstract
Abstract Objectives:Structured professional judgement is now the most widely accepted approach to clinical risk assessment and risk management. The Suicide Risk Assessment and Management Manual (S-RAMM) is a new structured professional judgement tool closely modelled on the HCR-20. This is the first prospective validation study for this instrument. Methods:Two post-membership registrars jointly interviewed 81 of 83 current inpatients to rate the S-RAMM. Two assistant psychologists independently rated the HCR-20, GAF and PANSS. All incidents of self-harm, attempted suicide, suicide and violence to others were collated from hospital reporting of critical incidents over the next six months supplemented by examination of other records. Results:For combined self-harm and suicide outcomes, the S-RAMM total score using the receiver operating characteristic had an area under the curve AUC=0.89, (95% CI 0.79 to 0.99). The S-RAMM performed as well for the prediction of self-harm and suicide as the HCR-20 did for violence, and better than measures of mental state (PANSS total score) and global function (GAF). Conclusions:The S-RAMM has better than minimum acceptable characteristics for use as a clinical or research tool for suicide risk assessment, and performs almost as well as the HCR-20 does for violence. Further prospective studies are now required, in other populations.
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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 | 0.019 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".