Long-term effects of risperidone<i>versus</i>haloperidol on verbal memory, attention, and symptomatology in schizophrenia
Bibliographic record
Abstract
There is evidence in the literature that cognitive functions in schizophrenia (SC) may be improved by atypical neuroleptics (NLPs) in contrast to typical medication, but there is still controversy regarding this apparent superiority of atypical drugs. In this study, we assessed the differential effects of risperidone and haloperidol on verbal memory, attention, and psychiatric symptoms in SC. The performance of 28 SC participants, randomly assigned to risperidone (2-6 mg/day) or haloperidol (2-40 mg/day), was compared with that of healthy controls. The California Verbal Learning Test (CVLT), the d2 Cancellation Test, and the Positive and Negative Symptoms Scale were administered at baseline and 3, 6, and 12 months. Relative to controls, all SC participants showed markedly impaired verbal memory and processing speed at each assessment period. There was no differential effect between the two NLPs on CVLT and d2 performance. However, risperidone was more effective than haloperidol in reducing psychiatric symptoms. Improvement in symptom severity was not associated with improvement in neurocognitive performance on these specific tests. Neither conventional nor atypical neuroleptic medications improved neurocognitive functioning over a 12-month follow-up, suggesting that psychopathological improvement under risperidone is independent of cognitive function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".