A meta-analysis of neuropsychological change to clozapine, olanzapine, quetiapine, and risperidone in schizophrenia
Bibliographic record
Abstract
Cognitive impairment is a core feature of schizophrenia and a major impediment to social and vocational rehabilitation. A number of studies have claimed cognitive benefits from treatment with various atypical antipsychotic drugs (APDs). The currently available evidence supporting cognitive improvement with atypical APDs was evaluated in two meta-analyses. Studies that (1) prospectively examined cognitive change to the atypical APDs clozapine, olanzapine, quetiapine, and risperidone, (2) included a commonly used neuropsychological test, and (3) provided data from which relevant effect sizes could be calculated, were included. Forty-one studies met these criteria. Neuropsychological test data from each study were combined into a Global Cognitive Index and nine cognitive domain scores. Two meta-analyses were carried out. The first included 14 controlled, random assignment trials that assigned subjects to an atypical APD and a typical APD control arm. The second analysis included all prospective investigations of atypical treatment and the within-group change score divided by its standard deviation served as an estimate of effect size (ES). The first analysis revealed that atypicals are superior to typicals at improving overall cognitive function (ES=0.24). Specific improvements were observed in the learning and processing speed domains. The second analysis extended the improvements to a broader range of cognitive domains (ES range=0.17-0.46) and identified significant differences between treatments in attention and verbal fluency. Moderator variables such as study blind and random assignment influence results of cognitive change to atypical APDs. Atypical antipsychotics produce a mild remediation of cognitive deficits in schizophrenia, and specific atypicals have differential effects within certain cognitive domains.
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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".