Assessing Suicide Risk: What is Commonly Missed in the Emergency Room?
Bibliographic record
Abstract
OBJECTIVE: Although risk assessment for suicide has been extensively studied, it is still an inexact process. The current study determined how busy emergency clinicians actually assessed and documented suicide risk, while also examining the differences between psychiatric and emergency medicine opinions on the importance of various suicide predictors. METHOD: Phase 1 of the study involved the administration of a survey on the relative importance of various suicide predictors for the specialties of psychiatry and emergency medicine. In phase 2 of the study, a chart review of psychiatric emergency room patients was conducted to determine the actual documentation rates of the suicide predictors. RESULTS: Several predictors that were deemed to be important, including suicidal plan, intent for suicide, having means available for suicide, and practicing suicide (taking different steps leading up to suicide but not actually attempting suicide), had low documentation rates. CONCLUSIONS: Medical specialties have different opinions on the importance of various suicide predictors. Also, some predictors deemed important had low documentation rates. Educational interventions and simple assessment tools may help to increase documentation rates of several suicide predictors in busy clinical settings.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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 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".