Working memory and depressive symptoms in patients with schizophrenia and substance use disorders
Bibliographic record
Abstract
BACKGROUND: Substance abuse is highly prevalent in schizophrenia and it has been associated with negative consequences on the course of the pathology. Regarding cognition, the prevailing literature has produced mixed results. Some groups have reported greater cognitive impairments in dual diagnosis schizophrenia, while other groups have described the reverse. OBJECTIVE: The current cross-sectional study sought to investigate the potential differences in psychiatric symptoms and cognition between schizophrenia patients with and without substance use disorders. METHODS: Fifty-three schizophrenia patients were divided into two groups: with (n=30) and without (n=23) a substance use disorder (DSM-IV criteria). Psychiatric symptoms were measured with the Positive and Negative Syndrome Scale (PANSS) and the Calgary Depression Scale for Schizophrenia (CDSS). Psychomotor speed and spatial working memory were measured using Cambridge Neuropsychological Tests Automated Battery (CANTAB). RESULTS: Patients in the dual diagnosis group displayed more severe depressive symptoms and poorer strategy during the working memory task. CONCLUSIONS: These results are in keeping with the prevailing literature describing negative consequences of substance abuse in schizophrenia. Substance abuse may exacerbate depressive symptoms and interfere with metacognition in schizophrenia.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".