Assessment of suicide risk in schizophrenia with addictive comorbidity
Bibliographic record
Abstract
Introduction Comorbid drug use disorders are associated with greater risk for relapse in schizophrenia and lower adherence to treatment. A comprehensive evaluation of patients with dual diagnosis should address the problem of suicide risk, which is a reputated complication of both psychotic disorders and drug use disorders. Objectives Depression and suicide risk assessment in subjects diagnosed with schizophrenia and drug related disorders. Aims To establish a protocol for early intervention in cases with depressive features that associate suicide risk. Methods All the patients ( n =37, female n =15, male n =12) with both schizophrenia and a drug related disorder, consecutively admitted in our department during a 6-month period, were evaluated using Calgary Depression Scale for Schizophrenia (CDSS), Positive and Negative Syndrome Scale (PANSS), Inventory of Drug Taking Situations (IDTS), Clinical Global Impression- Severity (CGI-S). Subjects were evaluated at admission, discharge and after 3 months. Results A percentage of 21.6 of all patients registered CDSS score at baseline above the cutt-off score for a major depressive episode of 6, while 10.8% had CDSS score of 6 and 8.1% had a CDSS score of 5. IDTS had greater scores in all these 15 patients with high CDSS values, comparative to the other, P Conclusions Using a specific method for depression and suicide risk in patients with schizophrenia and drug related disorder is very useful for establishing a specific treatment approach and monitoring plan.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".