Attempted Suicide: Factors Leading to Hospitalization
Bibliographic record
Abstract
OBJECTIVE: This study analyzes how sociodemographic and clinical characteristics influence the treatment decision for patients referred to a university hospital emergency room (ER) owing to attempted suicide. METHOD: Using a cross-sectional design, we monitored all patients admitted to a university hospital ER after attempting suicide, over a 3-year period (n = 404). Treatment decisions were categorized into 3 groups: inpatient treatment, outpatient treatment, and no further treatment. RESULTS: Older patients were more likely to be hospitalized, while women and patients with regular occupational activity were more likely to receive outpatient treatment. In logistic regression analysis, attempted suicide using aggressive methods, history of psychiatric inpatient treatment, and psychotic disorders were associated with inpatient treatment. Adjustment and neurotic disorders were related to outpatient treatment. CONCLUSIONS: The decision to hospitalize can be satisfactorily predicted by means of sociodemographic and clinical characteristics, while the number of patients assigned to outpatient treatment is underestimated. A triage that relies only on sociodemographic and clinical data as well as risk factors could result in too frequent admissions of patients after attempted suicide.
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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 | 0.000 | 0.006 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".