Does cognitive learning potential measured with the dynamic Wisconsin Card Sorting Test predict rehabilitation outcome in elderly patients post-stroke?
Bibliographic record
Abstract
Objective To determine whether cognitive learning potential measured with the dynamic Wisconsin Card Sorting Test has added value in predicting rehabilitation outcome in elderly patients post-stroke after controlling for age, ADL independence at admission, global cognitive functioning and depressive symptoms.Methods Participants were patients with stroke admitted to a geriatric rehabilitation unit. ADL independence (Barthel Index) at discharge was used as measure for rehabilitation outcome. Predictor variables included age, ADL independence at admission, global cognitive functioning (Montreal Cognitive Assessment), depressive symptoms (Geriatric Depression Scale) and cognitive learning potential measured with the dWCST.Results Thirty participants were included. Bivariate analyses showed that rehabilitation outcome was significantly correlated with ADL independence at admission (r = 0.443, p = 0.014) and global cognitive functioning (r = 0.491, p = 0.006). Regression analyses showed that the dWCST was not an independent predictor of rehabilitation outcome. ADL independence at admission was the only significant predictor of rehabilitation outcome (beta = 0.480, p = 0.007).Conclusions Cognitive learning potential, measured with the dWCST has no added value in predicting rehabilitation outcome in elderly patients post-stroke. ADL independence at admission was the only significant predictor of rehabilitation outcome.Registration number Netherlands Trial Register Trial NL7947.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".