The impact of atypical antipsychotic medications on long-term memory dysfunction in schizophrenia spectrum disorder: a quantitative review
Bibliographic record
Abstract
This meta-analytic review examines the efficacy of antipsychotic medications in ameliorating schizophrenia-related long-term memory (LTM) impairments. Twenty-three studies were reviewed that compared schizophrenia spectrum patients treated (a) with atypical versus typical antipsychotic medications, or (b) with various atypical treatments. In 17 atypical versus typical trials aggregating 939 participants, superior overall (verbal and nonverbal) LTM was detected in patients assigned to atypical trials. However, this difference was small (effect size estimate (ES) 0.17; 95% Confidence Interval (CI) 0.04 to 0.31) and specific to certain atypical treatments. Relative to typical antipsychotic trials, LTM superiority was marginally significant for risperidone trials (ES 0.20; 95% CI -0.03 to 0.44) and significant for olanzapine trials (ES 0.29; 95% CI 0.08 to 0.49). In contrast, clozapine trials did not produce a LTM advantage over typical trials (ES -0.06; 95% CI -0.35 to 0.23). Due to the lack of available studies, the effect of quetiapine was indeterminate. Direct comparison between atypical trials revealed a similar effect pattern. A marginally significant superiority in overall LTM was detected for risperidone and olanzapine compared to clozapine (ES 0.28; 95% CI -0.04 to 0.59), which reached significance for verbal LTM (ES 0.36; 95% CI 0.04 to 0.67). Finally, the beneficial impact of antipsychotic medications emerged as a function of differences in the anticholinergic properties of the treatment arms being compared.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.016 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".