Development of a measure of functioning for stroke recovery: The functional recovery measure
Bibliographic record
Abstract
PURPOSE: To develop a parsimonious measure of functioning for persons after stroke. METHOD: A sub-set of 206 community-dwelling subjects with a first stroke from a larger cohort was interviewed within 9 months using 39 items from five indices assessing functioning. Information was collected on influencing variables: age, stroke type and severity, and previous health. Two statistical methods, factor analysis and Rasch analysis, confirmed the item structure, hierarchy and dimensionality of the measure. Statistics confirmed fit to the model; internal consistency was also assessed. Items were deleted iteratively based on fit and relationship to the construct. RESULTS: The subjects were predominately male (63%) aged on average 68-years-old. A 12-item unidimensional functioning measure was developed. All items and persons fit the Rasch model with stable item-person reliability indices of 0.98 and 0.91, respectively. Item precision (standard errors) ranged from 0.14-0.37 logits. Gaps in measurement occurred at the extremes of the measure and there was a small ceiling effect. CONCLUSIONS: A 12-item measure captured the concept of functioning that could be used as a prototype to quantify recovery post-stroke. These items could form the basis for a measure of functioning.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".