Cognitive remediation for older community‐dwelling individuals with schizophrenia: a pilot and feasibility study
Bibliographic record
Abstract
OBJECTIVE: Cognitive deficits are among the strongest predictors of function in individuals with schizophrenia. This relationship continues to be strong as these individuals grow older into their eight decade. Cognitive remediation (CR) improves cognition in individuals with schizophrenia. This study aims at assessing the feasibility and potential effect of CR in patients with schizophrenia 60 years of age or older. METHODS: We adapted a CR protocol involving restorative and strategy-based methods over four cohorts of older outpatients with schizophrenia to target cognitive deficits associated with aging and schizophrenia. CR was provided in eight, 2-h weekly didactic sessions and online at-home exercises. Computerized drill and practice exercises were used with bridging to activities of daily life. Computer exercise selection and difficulty level parameters optimized adherence. Progression individually determined difficulty levels. We modified computer laboratory ergonomics to accommodate mobility needs. Participants were assessed at baseline and end-of-study using clinical and cognitive assessments. RESULTS: Twenty-two participants enrolled: 18 (mean [SD] age: 69.8 [5.3]) completed CR. Mean (SD) global cognition T score from the Measurement and Treatment Research to Improve Cognition in Schizophrenia Consensus Cognitive Battery was 27.7 (10) at baseline and 28.8 (9.7) at the completion of the study. These means are over 2 SD below the norms. The change in global cognition was not statistically significant (paired t(17) = -1.18, p = 0.25). CONCLUSIONS: Our pilot study suggests that CR is well tolerated by most older outpatients with schizophrenia. Future studies need to assess whether increasing the frequency or the number of CR sessions leads to significant improvement in cognition.
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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 | 0.003 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".