Cognitive remediation for patients with late-life schizophrenia: A follow-up pilot study
Bibliographic record
Abstract
OBJECTIVES: The number of older adults with schizophrenia ("late-life schizophrenia" (LLS)) is growing with the aging of the general population. Cognitive impairment in adults with LLS predicts functioning. Cognitive remediation (CR) improves cognition in schizophrenia, however literature in LLS is sparse. Anticholinergic burden (ACB) impacts CR effects. We previously showed that CR is feasible in LLS but did not produce a promising cognitive effect. This study examined the feasibility, tolerability, and effect of an intensive, prolonged and flexible CR on overall and specific cognitive functions in LLS. We also assessed ACB impact on CR effect on global cognition. DESIGN: Pre-post intervention SETTING PARTICIPANTS: Tertiary care outpatients with LLS INTERVENTION: We adapted the CR protocol from our previous pilot study, providing CR over 24, twice-weekly, therapist-guided group sessions that combined computerized drill-and-practice exercises with skills transference strategies, with additional time allocated for exercise practice. MEASUREMENTS: We assessed participants at baseline and at study completion using clinical and cognitive measures. RESULTS: Thirty-four participants (mean (SD) age = 65.8 (5.7)) attended at least one CR session, 25 participants completed baseline and follow-up assessments, and 20 participants completed at least 75 % of the CR sessions. There was no time effect on global cognition, although there was an interaction with ACB. There was also a pattern of improvement in executive function across several cognitive tests. CONCLUSIONS: An intensive, prolonged and flexible CR was feasible and well-tolerated, showing promise in improving executive function of patients with LLS. Larger and randomized controlled trials are needed in this population.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".