Efficacy of Aripiprazole in Enhancing Cognitive Functions in Psychiatric Disorders: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Background: Psychiatric disorders have many symptoms including decline in cognitive symptoms. Parkinson’s disease significantly affects the patient quality of life. Aripiprazole, which is atypical antipsychotic, has a unique mechanism of action that may improve cognitive functions due to its activity at dopamine D2 receptors. Materials and Methods: We adhered to Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines and conducted a comprehensive search across many databases including PubMed, Scopus, Web of Science and Cochrane Library. We focused on randomised controlled trials and cohort studies that investigated the cognitive effects of Aripiprazole. Data from selected studies were extracted and analysed using RevMan and we used ROB2 and Newcastle-Ottawa Scale tools for quality and bias assessment. Results: Our meta-analysis included 751 patients from nine studies. Results indicated that Aripiprazole significantly improved working memory (standardised mean difference [SMD] 0.48, 95% confidence interval [CI] [0.18, 0.78], P = 0.002) and cognitive flexibility as measured by Trails A (SMD − 0.27, 95% CI [−0.49, −0.05], P = 0.02) and the Wisconsin Card Sorting Test Perseverations errors (SMD − 0.42, 95% CI [−0.69, −0.15], P = 0.003). However, no significant changes were observed in verbal learning (SMD 0.15, 95% CI [−0.10, 0.40], P = 0.23), verbal fluency (SMD − 0.05, 95% CI [−0.32, 0.22], P = 0.71) or performance in Trails B (SMD 0.10, 95% CI [−0.25, 0.45], P = 0.58). The Wechsler Adult Intelligence Scale-Revised Digit Symbol test also showed no significant improvement (SMD − 0.02, 95% CI [−0.28, 0.24], P = 0.86). Conclusion: Our findings demonstrated that Aripiprazole has a significant positive effect on specific cognitive functions such as working memory and cognitive flexibility in psychiatric patients. This supports its role in utility as a cognitive enhancer.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.045 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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".