Violent behaviour in early psychosis patients: Can we identify clinical risk profiles?
Bibliographic record
Abstract
AIMS: The objective of this study is to explore, within a sample of early psychosis patients (EPP), if subgroups regarding rate of violent behaviour (VB) against others can be identified on the basis of dynamic risk factors (treatment modifiable characteristics). METHODS: In a sample of 265 EPP, treated at the Treatment and Early Intervention in Psychosis Program in Lausanne, we conducted a latent-class analysis on the basis of the main dynamic VB risk factors (substance use disorder [SUD], positive symptoms, insight, and impulsivity). VB were restricted to "serious violence" and were assessed through patients self-report, interview with relatives or forensic services and with a standardized instrument. RESULTS: The analysis confirmed the heterogeneity of the sample regarding rate of VB. Patients could be stratified within 4 subgroups, 3 of which were at increased risk of VB. The two groups with the highest rates of VB displayed specific clinical profiles. The first one was characterized by high levels of impulsivity, hostility, positive symptoms and SUD, and the second, by low level of insight and low social functioning. These patterns suggest that significant difficulties in social interaction may contribute to the emergence of aggressive reactions against others. CONCLUSIONS: Identification of EPP at increased risk of VB seems possible on the basis of dynamic risk factors. If confirmed prospectively, this could pave the way to the development of preventive strategies and specific interventions.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".