Implication of substance use in suicidal or violent behaviours in a first episode psychosis spectrum disorder population : A 45 patients retrospective study
Bibliographic record
Abstract
Introduction In First Episode Psychosis (FEP), Suicidal Behaviours (SB), Violent Behaviours (VB) and substance use are frequent respectively 10% to 30%, 34.5% and 50% (Pompili et al., 2011), (Tournier et al., 2013). The role of substance use in facilitating SB and VB is described (Large et al., 2011). Objectives We aim to evaluate the impact of substance use in FEP patients. Our hypothesis is that substance use is associated with more SB or VB before first admission. Methods First admission files of 45 patients diagnosed ICD10 F20 to F29 during the 2013-2018 period were retrospectively studied. SB, VB and substance use (Cannabis, alcohol and opiate/cocaine) before admission were collected. Correlation between SB and VB were tested with cannabis, alcohol, opiate/cocaine use with chi2 Pearson independance test. Results The frequencies of suicidal behaviours and violent behaviours were 25 % and 22.7 %. The frequencies of cannabis use, alcohol use, opiate/cocaine use were 56.1 %, 10 % and 16.3 %. A strong significant correlation was found between opiate/cocaine use and violent behaviour, p = 0.011 Chi2 was 6.471 DF 1. No other significant correlations were found. Conclusions Suicidal behaviours and violent behaviours are known to be more frequent in psychotic patients with addictive comorbidity. Our french rural hospital retrospective study confirms that violent behaviours in first admission psychotic patients are strongly associated with opiate/cocaine substance use comorbidity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.000 |
| 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".