Pharmacological management of neurocognitive impairment in schizophrenia: A narrative review
Bibliographic record
Abstract
BACKGROUND: Cognitive impairment are among the core features of schizophrenia, experienced by up to 75% of patients. Available treatment options for schizophrenia including dopamine antagonists and traditional antipsychotic medications have not been shown to confer significant benefits on cognitive deficits. Contrary to the focus on management of positive symptoms in schizophrenia, cognitive abilities are main predictor of independent living skills, functional abilities, employment, engagement in relapse prevention, and patients' subjective sense of well-being and quality of life. This review aims to provide a summary of recent literature on pharmacological options for the treatment of cognitive deficits in schizophrenia. METHODS: We conducted a literature search of studies from 2011 to 2021 across four electronic databases including PubMed, PsycInfo, MEDLINE, and Embase. Human studies using a pharmacological treatment for cognitive impairment in schizophrenia were included. RESULTS: Fifty-eight eligible publications, representing 11 pharmacological classes, were included in this review. Major limitations involved small sample size, methodological limitations as well as heterogeneity of participants and outcome measures. CONCLUSIONS: Overall evidence remains inconclusive for any pharmacological classes studied for the treatment of cognitive deficits in schizophrenia. Methodological limitations in a majority of the studies rendered their findings preliminary. We further discuss possible explanations for these findings that could guide future research.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".