A meta-analysis of negative symptoms in dual diagnosis schizophrenia
Bibliographic record
Abstract
BACKGROUND: According to the self-medication hypothesis, schizophrenia patients would abuse psychoactive substances to get a relief from their negative symptoms. Studies testing the self-medication hypothesis in dual diagnosis (DD) schizophrenia have not been conclusive, with some studies showing that DD patients experience fewer negative symptoms, whereas other studies have failed to detect such differences. One potential confounding factor for this discrepancy lies in the diverse scales used to evaluate the negative symptoms. A systematic quantitative review of the literature using computerized search engines has been undertaken. METHOD: Studies were retained in the analysis if: (i) they assessed negative symptoms using the SANS; (ii) groups of schizophrenia patients were divided according to substance use disorders (alcohol, amphetamines, cannabis, cocaine, hallucinogens, heroin and phencyclidine). RESULTS: Attainable published studies were screened. According to our inclusion criteria, 18 possible studies emerged. Data from 11 studies were available for mathematical analysis. A moderate effect size (total n = 1135, 451 DD, 684 single diagnosis, adjusted Hedges' g = -0.470, p = 0.00001) was obtained, within a random-effect model, suggesting that DD patients experience fewer negative symptoms. Groups did not differ in age, sex, and positive/general psychopathology. CONCLUSIONS: Using narrow criteria (e.g. SANS), the results of this meta-analysis show that schizophrenia patients with a substance use disorder experience fewer negative symptoms than abstinent schizophrenia patients. As such, these results suggest either that substance abuse relieves the negative symptoms of schizophrenia or that the patients with fewer negative symptoms would be more prone to substance use 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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.054 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".