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Record W4405496247 · doi:10.4103/atmr.atmr_182_24

Efficacy of Risperidone in Enhancing Cognitive Functions in Psychiatric Disorders: A Systematic Review and Meta-analysis

2024· review· en· W4405496247 on OpenAlexaboutno aff
Khalid Nassir Almurayeh, Ahmed Fadeil Aleid, Dina Fares Alqahtani, Ibtihal Ibrahim Alkhudhayr, Maha Saleh Alyousef, Raghad Mushabab Al Ahmari, Rahaf H. Khoja, Fatema Hani Alawad, Mohammad Al Mohaini

Bibliographic record

VenueJournal of Advanced Trends in Medical Research · 2024
Typereview
Languageen
FieldMedicine
TopicSchizophrenia research and treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisRisperidonePsychiatryCognitionPsychologySystematic reviewMedicinePsychotherapistSchizophrenia (object-oriented programming)MEDLINEPolitical scienceInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background: Schizophrenia affects about 1% of the global population, with cognitive impairments significantly impacting patients’ quality of life. Risperidone, a second-generation antipsychotic, is commonly used to treat schizophrenia, known for its efficacy in reducing symptoms and potential cognitive benefits. However, the extent of its impact on cognitive functions remains unclear due to inconsistencies in the literature, necessitating a systematic review and meta-analysis to better understand these effects. Materials and Methods: Following Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines, we systematically reviewed clinical randomised trials and cohort studies evaluating the efficacy of risperidone on cognitive functions in schizophrenia. A comprehensive literature search was conducted in PubMed, Scopus, Web of Science and the Cochrane Library. The quality of included studies was assessed using the risk of bias 2 tool and the Newcastle–Ottawa Scale. Results: Eleven studies with 649 patients were analysed. Significant cognitive improvements were observed in several domains: positive and negative syndrome scale ( n = 228, standardised mean difference [SMD] = −0.44, 95% CI [−0.70, −0.17], P = 0.001), verbal learning ( n = 98, SMD = 0.67, 95% CI [0.26, 1.09], P = 0.001) and Trails B ( n = 124, SMD = −1.02, 95% CI [−1.28, −0.75], P < 0.00001). Wisconsin Card Sorting Test perseverations showed a reduction in errors ( n = 792, SMD = −0.14, 95% CI [−0.28, −0.0], P = 0.05), and Wechsler Memory Scale-Revised Logical Memory tests, both immediate ( n = 272, SMD = 0.53, 95% CI [0.28, 0.78], P < 0.0001) and delayed recall ( n = 288, SMD = 0.48, 95% CI [0.24, 0.72], P < 0.0001), indicated improvements. Attention and processing speed also improved significantly ( n = 98, SMD = 0.69, 95% CI [0.27, 1.11], P = 0.001). However, no significant effects were observed for working memory ( n = 368, SMD = −0.02, 95% CI [−0.23, 0.19], P = 0.85), verbal fluency ( n = 456, SMD = 0.04, 95% CI [−0.14, 0.22], P = 0.67), Wechsler Adult Intelligence Scale-Revised (WAIS-R) Block Design ( n = 78, SMD = 0.35, 95% CI [−0.10, 0.80], P = 0.13), WAIS-R digit symbol ( n = 170, SMD = 0.22, 95% CI [−0.09, 0.52], P = 0.16) or motor function ( n = 74, SMD = 0.27, 95% CI [−0.20, 0.75], P = 0.26). Conclusions: Our meta-analysis indicates that risperidone significantly improves cognitive functions in psychiatric disorders especially schizophrenia, particularly in areas related to attention, processing speed, executive function and memory recall. These findings highlight the potential of risperidone beyond its primary antipsychotic effects. Further research with larger and more diverse populations is needed to confirm these findings and explore the long-term impact of risperidone on cognitive functions.

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.529
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0100.002
Bibliometrics0.0110.014
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.004
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.128
GPT teacher head0.522
Teacher spread0.394 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designSystematic review
Domainnot available
GenreReview

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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