Meta-Analysis of Depressive Symptoms in Dual-Diagnosis Schizophrenia
Bibliographic record
Abstract
Substance abuse is highly prevalent in schizophrenia and associated with numerous negative consequences. While studies have regularly reported more severe depressive symptoms in addicted schizophrenia patients relative to non-abusing patients, some studies have not corroborated this finding. The current meta-analysis was performed to quantify the relative severity of depressive symptoms in dual-diagnosis schizophrenia. A search of the literature using computerized engines was undertaken. Studies were retained in the analysis if (i) they assessed depressive symptoms using validated scales specific to depression (e.g. Hamilton Depression Rating Scale); and (ii) groups of schizophrenia patients were divided according to substance use disorders (alcohol, amphetamines, cannabis, cocaine, hallucinogens, heroin and/or phencyclidine). According to the inclusion criteria, 20 studies were available for mathematical analysis. A small, positive and significant effect size estimate (n =3283; 1680 dual diagnosis; 1603 single diagnosis; adjusted Hedges's g =0.292; p =0.003) was obtained, within a random-effect model, suggesting that some dual-diagnosis patients experience more severe depressive symptoms than single-diagnosis patients. This significant difference was found only for studies using the Hamilton Depression Rating Scale but not for other depression scales. The results of the present meta-analysis suggest that addicted schizophrenia patients experience more severe depressive symptoms compared to non-abusing patients, but that the difference is smaller than commonly assumed. The meta-analysis also shows that the significance of results is related to the scale used to measure depressive symptoms. These results have methodological implications for future studies of depressive symptoms in dual-diagnosis patients, and potential implications for the prevention and treatment of depressive symptoms 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 | 0.016 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.062 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".