Can we usefully stratify patients according to suicide risk?
Bibliographic record
Abstract
In the UK, one in five adults has considered suicide at some time, and one in 15 has attempted suicide. Half of those who attempt suicide seek help afterwards—a quarter from a GP, a quarter from a hospital or specialist medical or psychiatric service. Suicidal patients; patients who present to health services with suicidal ideas, self harm, or suicide attempts; and patients who present as significantly distressed or mentally ill can be challenging to manage. Doctors are often advised to use suicide risk assessment to help them decide management plans. A wide variety of risk factors have been implicated in the stratification of potentially suicidal patients. This stratification is often expressed in terms of high, medium, or low-risk. In practice, doctors commonly give the greatest importance to suicidal ideation. In some specialist mental health settings these judgments are aided by local risk assessment forms composed of lists of clinical and demographic factors, while other centres use risk strata derived from validated questionnaires or scales. However, there is little consensus over their use and virtually no evidence that any of the method of suicide risk stratification can contribute to suicide prevention.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.034 | 0.155 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".