Verbal and visual learning ability in patients with treatment-resistant schizophrenia: A 1-year follow-up study
Bibliographic record
Abstract
Objective: In the general population, repeated cognitive testing produces learning effects with potential for improved test performance. It is currently unclear whether the same effect of repeated cognitive testing on cognition pertains to people living with schizophrenia, a condition often associated with significant cognitive impairments. This study aims to evaluate learning ability in people with schizophrenia and-considering the evidence that antipsychotic medication can additionally impair cognitive performance-explore the potential impact of anticholinergic burden on verbal and visual learning. Method: The study included 86 patients with schizophrenia, treated with clozapine, who had persisting negative symptoms. They were assessed at baseline, weeks 8, 24 and 52 using Positive and Negative Syndrome Scale, Hopkins Verbal Learning Test-Revised (HVLT-R) and Brief Visuospatial Memory Test-R (BVMT-R). Results: There were no significant improvements in verbal or visual learning across all measurements. Neither the clozapine/norclozapine ratio nor anticholinergic cognitive burden significantly predicted participants' total learning. Premorbid IQ was significantly associated with verbal learning on the HVLT-R. Conclusions: These findings advance our understanding of cognitive performance in people with schizophrenia and demonstrate limited learning performance in individuals with treatment-refractory schizophrenia.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.001 |
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".