Issues for selection of outcome measures in stroke rehabilitation: ICF Body Functions
Bibliographic record
Abstract
PURPOSE: To evaluate the psychometric and administrative properties of outcome measures assigned to the ICF Body Functions category, and commonly used in stroke rehabilitation research. METHOD: Critical review and synthesis of measurement properties for five commonly reported instruments in the stroke rehabilitation literature. Each instrument was rated using the eight evaluation criteria proposed by the UK Health Technology Assessment (HTA) programme. The instruments were also assessed for the rigour with which their reliability, validity and responsiveness were reported in the published literature. RESULTS: The reporting of specific measurement qualities for outcome instruments was relatively consistent across measures located within the same general ICF category. Far less information was available on the responsiveness of measures, compared with reliability and validity. The best available instruments were associated with the following body functions: cognitive impairment, depression and motor recovery. CONCLUSIONS: The reader is encouraged to examine carefully the nature and scope of outcome measurement used in reporting the strength of evidence for improved body functions in stroke rehabilitation since there is significant diversity. However there appears to be good consensus about what are the most important indicators of successful rehabilitation outcome in each domain of body function.
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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 | 0.646 | 0.862 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.001 | 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".