Cognitive effects of antipsychotic dosage and polypharmacy: a study with the BACS in patients with schizophrenia and schizoaffective disorder
Bibliographic record
Abstract
Antipsychotic polypharmacy and high doses have been associated with poorer outcome, longer hospital stays, and increased side effects. The present naturalistic study assessed the cognitive effects of antipsychotics in 56 patients with a diagnosis of schizophrenia or schizoaffective disorder, using the Brief Assessment of Cognition in Schizophrenia (BACS). Antipsychotic daily dose (ADD) was expressed as mg risperidone equivalents/day (RIS eq), using a model based on drug doses from the Clinical Antipsychotic Trials in Intervention Effectiveness (CATIE) study for second generation antipsychotics (SGA) and chlorpromazine equivalents for first generation antipsychotics (FGA), with a 1/1 equivalence between haloperidol and risperidone. Increasing age was associated with polypharmacy, FGA prescription and decreasing BACS score. FGA prescription, in turn, predicted a poorer cognitive functioning, independently of age, PANSS subscores and ADD. ADD was associated with decreasing cognitive scores, an effect that remained significant after controlling for age, PANSS or polypharmacy. The detrimental cognitive effects of polypharmacy, in turn, appeared to be mediated by ADD. Different methods of data fitting suggested that ADD above 5-6 mg RIS eq/day were associated with lower BACS scores. Overall, these results show that increasing antipsychotic daily dose is associated with poorer cognitive functioning at doses lower than previously thought, independently of the number of antipsychotic drugs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".