Comparing contents of functional outcome measures in stroke rehabilitation using the International Classification of Functioning, Disability and Health
Bibliographic record
Abstract
PURPOSE: To examine the content of outcome measures that are frequently used in stroke rehabilitation and focus on activities and participation, by linking them to the International Classification of Functioning, Disability and Health (ICF). Method. Constructs of the following instruments were linked to the ICF: Barthel Index, Berg Balance Scale, Chedoke McMaster Stroke Assessment Scale, Euroqol-5D, Functional Independence Measure, Frenchay Activities Index, Nottingham Health Profile, Rankin Scale, Rivermead Motor Assessment, Rivermead Mobility Index, Stroke Adapted Sickness Impact Profile 30, Medical Outcomes Study Short Form 36, Stroke Impact Scale, Stroke Specific Quality of Life Scale and Timed Up and Go test. Results. It proved possible to link most constructs to the ICF. Most constructs fitted into the activities and participation component, with mobility being the category most frequently covered in the instruments. Although instruments were selected on the basis of their focus on activities and participation, 27% of the constructs addressed categories of body functions. Approximately 10% of the constructs could not be linked. CONCLUSIONS: The ICF is a useful tool to examine and compare contents of instruments in stroke rehabilitation. This content comparison should enable clinicians and researchers to choose the measure that best matches the area of their interest.
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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 | 0.014 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".