Cognitive remediation therapy for participants with late‐life schizophrenia
Bibliographic record
Abstract
Abstract Background Older patients with schizophrenia have an elevated risk of dementia; enhancing their cognitive function holds promise for delaying dementia in this population. Cognitive deficits strongly predict function in schizophrenia. Anticholinergic medication burden (ACB) compounds age‐related cognitive declines in late‐life schizophrenia (LLS). Cognitive remediation (CR) improves cognition in schizophrenia, however literature in LLS is sparse. This study examined the effect of CR on cognitive performance in participants with LLS. We also assessed CR tolerability, the interaction between ACB and the effect of CR on cognition. Method We adapted CR from our pilot study to a larger group. CR was provided over 12, twice‐weekly, therapist‐guided group sessions. Computerized drill‐and‐practice exercise difficulty levels were adjusted automatically based on performance. Participants were assessed at baseline and at study completion using clinical and cognitive measures. Result Thirty‐four participants were enrolled, 20 (mean (SD) age: 66.0 [5.8]) completed 92.3% (22.14 [1.98]) CR sessions; the ITT group (N=34) attended 72.9% (17.50 [7.23]) sessions. Global cognition did not improve (completers: p=0.80, ITT: p=0.86) with negligible effect size (completers: d=0.08; ITT d=0.04). There was no significant improvement in executive function (completers: p=0.16; ITT: p=0.33). Medication ACB was inversely associated with total MoCA score, accounting for 8.9% of the variance in final MoCA score (p=0.02). Conclusion Overall, CR was well tolerated but did not improve global cognition or executive functioning in this outpatient sample with LLS. A higher ACB was associated with greater cognitive impairment and a poorer response to CR. Future studies need to better characterize potential variables serving to limit the response to CR, including the number and frequency of CR sessions and the role of ACB.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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".