Risk factors of suicide re-attempt: A two-year prospective study
Bibliographic record
Abstract
BACKGROUND: History of suicide attempt (SA) is the strongest predictor of a new SA and suicide. It is primordial to identify additional risk factors of suicide re-attempt. The aim of this study was to identify risk factors of suicide re-attempt in patients with recent SA followed for 2 years. METHODS: In this multicentric cohort of adult inpatients, the median of the index SA before inclusion was 10 days. Clinicians assessed a large panel of psychological dimensions using validated tools. Occurrence of a new SA or death by suicide during the follow-up was recorded. A cluster analysis was used to identify the dimensions that best characterized the population and a variable "number of personality traits" was created that included the three most representative traits: anxiety, anger, and anxious lability. Risk factors of re-attempt were assessed with adjusted Cox regression models. RESULTS: Among the 379 patients included, 100 (26.4 %) re-attempted suicide and 6 (1.6 %) died by suicide. The two major risk factors of suicide re-attempt were no history of violent SA and presenting two or three personality traits among trait anxiety, anger and anxious lability. LIMITATIONS: It was impossible to know if treatment change during follow-up occur before or after the re-attempt. DISCUSSION: One of the most important predictors of re-attempt in suicide attempters with mood disorders, was the presence of three personality traits (anger, anxiety, and anxious lability). Clinicians should provide close monitoring to patients presenting these traits and proposed treatments specifically targeting these dimensions, especially anxiety.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".