Bibliographic record
Abstract
Cognitive impairment is one of the core features in schizophrenia, which is closely related to functional impairment. Despite tremendous efforts to develop pro-cognitive drugs for schizophrenia, no cognitive enhancer is currently available. Beneficial effects of antipsychotic medication on cognition have remained controversial; in fact, both typical and atypical antipsychotics have been shown to induce cognitive impairment across various domains in healthy subjects as well as patients with schizophrenia. However, data on antipsychotic dosing strategy for improvement of cognitive function have been scarce. In this presentation, the presenter will review the available evidence showing the relationship between antipsychotic dose and cognitive impairment and discuss antipsychotic dosing strategy to achieve better cognitive function. To date, a body of evidence has suggested that higher dose of antipsychotics or excessive dopaminergic blockade impairs cognitive function in patients with schizophrenia, even treated with atypical antipsychotics. Furthermore, a recent randomized controlled trial demonstrated that dose reduction of risperidone or olanzapine by half improved cognitive function without significantly increasing the risk of relapse or clinical worsening for stable patients with schizophrenia over six months. These results highlight the fact that even atypical antipsychotics can induce cognitive impairment in a dose-dependent fashion, and underscore the need for using the lowest possible dose of typical or atypical antipsychotics to minimize or prevent such cognitive side effects.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".