Rating of Everyday Arm-Use in the Community and Home (REACH) Scale for Capturing Affected Arm-Use after Stroke: Development, Reliability, and Validity
Bibliographic record
Abstract
OBJECTIVE: To develop a brief, valid and reliable tool [the Rating of Everyday Arm-use in the Community and Home (REACH) scale] to classify affected upper limb use after stroke outside the clinical setting. METHODS: Focus groups with clinicians, patients and caregivers (n = 33) and a literature review were employed to develop the REACH scale. A sample of community-dwelling individuals with stroke was used to assess the validity (n = 96) and inter-rater reliability (n = 73) of the new scale. RESULTS: The REACH consists of separate scales for dominant and non-dominant affected upper limbs, and takes five minutes to administer. Each scale consists of six categories that capture 'no use' to 'full use'. The intraclass correlation coefficient and weighted kappa for inter-rater reliability were 0.97 (95% confidence interval: 0.95-0.98) and 0.91 (0.89-0.93) respectively. REACH scores correlated with external measures of upper extremity use, function and impairment (rho = 0.64-0.94). CONCLUSIONS: The REACH scale is a reliable, quick-to-administer tool that has strong relationships to other measures of upper limb use, function and impairment. By providing a rich description of how the affected upper limb is used outside of the clinical setting, the REACH scale fills an important gap among current measures of upper limb use and is useful for understanding the long term effects of stroke rehabilitation.
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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 | 0.006 | 0.013 |
| 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".