Responsiveness and trajectory of changes in the rating of everyday arm-use in the community and home (REACH) scale over the first-year post-stroke
Bibliographic record
Abstract
OBJECTIVE: To examine the trajectory of the Rating of Everyday Arm-use in the Community and Home (REACH) scores over the first-year post-stroke, determine if REACH scores are modified by baseline impairment level and explore the responsiveness of the REACH scale through hypothesis testing. DESIGN: Consecutive sample longitudinal study. SETTING: Participants were recruited from an acute stroke unit and followed up at three, six, and 12 months post-stroke. PARTICIPANTS: Seventy-three participants with upper limb weakness (Shoulder Abduction and Finger Extension score ≤ 8). MAIN MEASURES: The REACH scale is a six-level self-report classification scale that captures how the affected upper limb is being used in one's own environment. The Fugl-Meyer Upper Limb Assessment (FMA-UL), Stroke Upper Limb Capacity Scale (SULCS), accelerometer-based activity count ratio and Global Rating of Change Scale (GRCS) were used to capture upper limb impairment, capacity, and use. RESULTS: The following proportions of participants improved at least one REACH level: 64% from baseline to three months, 37% from three to six months and 13% from six to 12 months post-stroke. The trajectory of REACH scores over time was associated with baseline impairment. Change in REACH had a moderate correlation to change in SULCS and the GRCS but not FMA-UL or the activity count ratio. CONCLUSIONS: Results of hypothesis testing provide preliminary evidence of the responsiveness of the REACH scale. On average, individuals with severe impairment continued to show improvement in use over the first year, while those with mild/moderate impairment plateaued and a small proportion decreased in the early chronic phase.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".