Cognitive–behavioural therapy for the symptoms of schizophrenia: systematic review and meta-analysis with examination of potential bias
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Bibliographic record
Abstract
BACKGROUND: Cognitive-behavioural therapy (CBT) is considered to be effective for the symptoms of schizophrenia. However, this view is based mainly on meta-analysis, whose findings can be influenced by failure to consider sources of bias. AIMS: To conduct a systematic review and meta-analysis of the effectiveness of CBT for schizophrenic symptoms that includes an examination of potential sources of bias. METHOD: Data were pooled from randomised trials providing end-of-study data on overall, positive and negative symptoms. The moderating effects of randomisation, masking of outcome assessments, incompleteness of outcome data and use of a control intervention were examined. Publication bias was also investigated. RESULTS: Pooled effect sizes were -0.33 (95% CI -0.47 to -0.19) in 34 studies of overall symptoms, -0.25 (95% CI -0.37 to -0.13) in 33 studies of positive symptoms and -0.13 (95% CI -0.25 to -0.01) in 34 studies of negative symptoms. Masking significantly moderated effect size in the meta-analyses of overall symptoms (effect sizes -0.62 (95% CI -0.88 to -0.35) v. -0.15 (95% CI -0.27 to -0.03), P = 0.001) and positive symptoms (effect sizes -0.57 (95% CI -0.76 to -0.39) v. -0.08 (95% CI -0.18 to 0.03), P<0.001). Use of a control intervention did not moderate effect size in any of the analyses. There was no consistent evidence of publication bias across different analyses. CONCLUSIONS: Cognitive-behavioural therapy has a therapeutic effect on schizophrenic symptoms in the 'small' range. This reduces further when sources of bias, particularly masking, are controlled for.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it