Measurement properties of the Reaching Performance Scale for Stroke
Bibliographic record
Abstract
AIM: Reaching Performance Scale for Stroke (RPSS) evaluates the upper limb reach-to-grasp movement quality and compensatory movements. The objective of the study was to test the reliability, construct validity, and interpretability of the Brazilian-Portuguese RPSS. METHODS: Fifty-one individuals (mean age 62 ± 10.8 years), with acute-to-chronic stroke (range: 1-300 months) were video recorded while performing a reach-to-grasp task of a cone placed both close and far from the individual. Their degree of motor impairment ranged from 4 to 59 points in the Fugl-Meyer scale. RESULTS: < 0.0001). The scale was able to discriminate individuals with mild or moderate upper limb impairment from those with severe impairment. We found ceiling and floor effects. CONCLUSIONS: Reaching Performance Scale for Stroke showed excellent reliability and redundant internal consistency. The construct validity with the Fugl-Meyer scale was strong. Reaching Performance Scale for Stroke was able to discriminate individuals with different levels of upper limb impairment.IMPLICATIONS FOR REHABILITATIONPost-stroke individuals develop compensatory strategies to perform reaching movements with the paretic upper limb.The Reaching Performance Scale for Stroke provides a quantitative and qualitative evaluation of the reach-to-grasp movement.The Reaching Performance Scale for Stroke is suitable for use with Brazilian-Portuguese speakers and has adequate reliability and validity.
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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 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
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".