Impact of Substance Use Disorder on Presentation and Short-Term Course of Schizophrenia
Bibliographic record
Abstract
The aim of the present study was to compare a cohort of schizophrenia patients with substance use disorder (SUD) with a similar cohort of schizophrenia patients without SUD with regard to sociodemographic variables, clinical variables, psychopathology, anxiety symptoms, depressive symptoms, treatment outcome, and side effect profile of drugs. A total of 143 consecutive inpatients with ICD-10 DCR diagnosis of schizophrenia were included after obtaining informed consent. Patients were evaluated by a semistructured data sheet and Maudsley Addiction Profile. They were then rated by Positive and Negative Symptoms Scale, Calgary Depression Scale, Hamilton Anxiety Rating Scale, and Brief Psychiatric Rating Scale at presentation, three weeks, and six weeks. At three weeks and six weeks, they were also evaluated by UKU Side Effect Rating Scale. Substance abuse was detected in 63.6% schizophrenia patients. Nicotine was the commonest substance followed by cannabis and alcohol. Substance users had longer untreated illness and more depressive symptoms at presentation and six-week follow-up. Dual diagnosis patients had difficulty in abstraction at three and six weeks but not at presentation. Schizophrenia patients with SUD had more depressive symptoms. SUD appeared to mask abstraction difficulties at presentation. Schizophrenia patients with SUD should be carefully assessed for presence of depression.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".