Cognitive Rehabilitation After Stroke: A Case Series Testing a New Method to Transfer Gains to Daily Life
Bibliographic record
Abstract
This case-series piloted a novel cognitive rehabilitation approach, i.e., Constraint-Induced Cognitive Therapy (CICT), for improving Instrumental Activities of Daily Living (IADL) in stroke survivors with chronic, mild-to-moderate, cognitive impairment. Four consecutively sampled stroke survivors (mean chronicity=18 months, SD=10) with mild (n=3) or moderate (n=1) cognitive impairment received 35 hours of CICT. CICT combined two empirically supported approaches: Speed of Processing Training (SOPT) and behavior change techniques from Constraint-Induced Movement Therapy (CIMT) adapted to transfer gains from in-lab cognitive training to daily life. The latter featured IADL training following shaping principles and a suite of behavioral techniques, called the Transfer Package, to promote participation in cognitively-based functional activities outside of the lab. Outcome measures assessed cognitive processing speed (Useful Field of View, UFOV) and IADL performance outside the treatment setting (Canadian Occupational Performance Measure, COPM). All three participants with valid UFOV data displayed meaningful improvements after treatment in cognitive processing speed (M=64%, SD=40, d'=1.58). All three with COPM data reported meaningful improvements in satisfaction with IADL performance (M=2.87, SD=1.5, d'=1.91). Values of d' ≥0.57 are large. Per structured interviews developed for this study, the IADL improvements present after treatment lasted for at least a year. These promising findings warrant further study.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| 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".