Dissociating medication effects from learning and practice effects in a neurocognitive study of schizophrenia: Olanzapine versus haloperidol
Bibliographic record
Abstract
OBJECTIVE: To contrast the effect of a typical antipsychotic (haloperidol) and an atypical antipsychotic (olanzapine) on neurocognitive functioning in schizophrenia when learning and practice (LP) effects are controlled. METHODS: Two groups of participants were recruited, 27 schizophrenia patients in their first 5 years of illness and 13 normal controls. Prior to double-blind randomisation, all subjects were assessed on four occasions within 5 days (prerandomisation period) on the same neurocognitive battery. Repeated assessment prior to randomisation was chosen as a method to control for LP effects. Patients were then randomised to 56 days of treatment with haloperidol or olanzapine (postrandomisation). All subjects were assessed on neurocognitive measures at Days 28 and 56. RESULTS: LP effects were present during the prerandomisation period on motor tasks, verbal and visual short-term memory, attention, and on a measure of verbal working memory. There were no changes in performance for patients randomised to treatment with olanzapine or haloperidol or the normal control group during the postrandomisation period. CONCLUSIONS: Once LP effects are controlled, olanzapine and haloperidol do not affect performance on measures of motor functioning, verbal short-term memory, attention, verbal working memory, reaction time, visuospatial short-term memory, and visual working memory beyond that observed from LP 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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".