Which approach to measure cognitive functioning should be preferred when exploring the association between cognitive functioning and participation after stroke?
Bibliographic record
Abstract
A variety of approaches are currently used to explore the relationship between cognitive functioning and participation after stroke. We aimed to gain insight into the preferred approach to measure cognitive functioning when exploring the association between cognitive functioning and participation in the long term after stroke. In this inception cohort study 128 individuals with stroke participated and were assessed at a single time point three to four years after the event. Participation was measured using the Restrictions subscale of the Utrecht Scale for Evaluation of Rehabilitation-Participation. Subjective cognitive complaints were assessed using the Cognition subscale of the Checklist for Cognitive and Emotional Consequences (CLCE-24-C). Objective cognitive performance was measured using the Montreal Cognitive Assessment (MoCA) and a neuropsychological test battery (NTB) testing multiple cognitive domains. Participation showed a strong correlation (r = 0.51) with the CLCE-24-C and moderate correlations with the domains of visuospatial perception (r = 0.37) and mental speed (r = 0.36). Backward linear regression analyses showed that participation restrictions were best explained by the combination of the CLCE-24-C and a test for visuospatial perception (R2 = 0.31). Our findings suggest the use of a combination of subjective cognitive complaints and objective cognitive performance to explore the relationship between cognitive functioning and participation after stroke.
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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 | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".