Predictors of prospective suicide attempts in a group at risk of personality disorder following self-poisoning
Bibliographic record
Abstract
Background Patients with personality disorder (PD) are at risk for suicidal behavior and are frequently admitted for this reason to emergency departments. In this context, researchers have tried to identify predictors of their suicidal acts, however, the studies have been mostly retrospective, and uncertainty remains. To prospectively explore factors associated with suicide attempts (SA) in individuals screened for PD from the ecological context of emergencies. Methods Patients were recruited from two emergency departments after a self-poisoning episode (n = 310). PDQ-4+ (risk of PD), TAS-20 (alexithymia), SIS (suicidal intent), H (hopelessness), BDI-13 (depression), AUDIT (alcohol consumption), and MINI (comorbidity) questionnaires were completed. SA over the subsequent two years were identified by mailed questionnaires and hospitals’ active files. Logistic regression analyses were performed. Results Having a previous suicidal attempt was linked to a 2.7 times higher chance of recurrence after 6 months, whereas the TAS-20 showed a 1.1 times higher risk at 18 months (OR = 1.1) and the BDI at 24 months (OR = 1.2). Each one-unit increment in TAS-20 and BDI-13 scores increased the risk of SA by 9.8 and 20.4% at 18 and 24 months, respectively. Conclusion Some clinical features, such as alcohol dependence, suicide intent, and hopelessness, may not be reliable predictors of SA among PD patients. However, in the short term, previous SA and, in the long term, depression and alexithymia may be the most robust clinical predictors to consider in our sample of patients with self-poisoning SA. Clinical trial registration: [ ClinicalTrials.gov ], NCT00641498 24/03/2008 [#2006-A00450-51].
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".