Comparative clinical and sociodemographic assessment of substance use in first episode, drug-naïve psychosis and schizophrenia patients
Bibliographic record
Abstract
Objective: This study aims to compare recent-onset, drug-naïve patients with first-episode psychosis (FEP) and patients with schizophrenia in terms of substance and smoking history, and to explore their associations with sociodemographic and clinical characteristics.Methods: A total of 107 patients were included: 56 with drug-naïve FEP and 51 with schizophrenia. Standardized clinical instruments were used, including the Clinical Global Impression–Severity Scale (CGI-S), the Scale for the Assessment of Negative Symptoms (SANS), the Scale for the Assessment of Positive Symptoms (SAPS), and the Calgary Depression Scale for Schizophrenia (CDSS).Results: Substance use was more prevalent among schizophrenia patients (41.2%) compared to FEP patients (25.0%). In both groups, substance use was associated with increased smoking, alcohol consumption, and greater clinical severity. Specifically, FEP patients with substance use reported significantly higher depressive and negative symptoms, as well as greater illness severity. Among schizophrenia patients, substance use was correlated with elevated SAPS, SANS, and CGI-S scores, as well as higher rates of self-mutilation.Conclusion: Substance use contributes to greater symptom burden, behavioral dysregulation, and overall clinical severity in both FEP and schizophrenia. Early screening and the integration of dual-diagnosis treatment strategies are essential to mitigate adverse outcomes in psychotic disorders.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".