Assessment of capacity for myoelectric control: Evaluation of construct and rating scale
Bibliographic record
Abstract
OBJECTIVE: To examine the construct and rating scale of the Assessment of Capacity for Myoelectric Control, an assessment to evaluate ability in using a prosthetic hand. DESIGN: Cross-sectional study. SUBJECTS: Upper limb prosthesis users with different prosthetic levels/sides and prosthetic experience were included (n = 96). METHODS: Subjects' assessments with the Assessment of Capacity for Myoelectric Control were collected by 6 raters during their regular hospital visits. Rasch analysis was used, since it allowed an analysis of the data at the item and category levels. Dimension, item hierarchy and item fit statistics were used to examine the construct. Different Rasch parameters were used to examine rating scale structure and its use. RESULTS: The consistency of item difficulties with clinical knowledge and the unidimensionality confirmed that the construct is valid. Two items functioned unexpectedly (misfit), but the misfit was idiosyncratic to the sample, not systematic to the items. The 4-point rating scale usefully differentiated the subjects on the basis of their abilities. The use of category 2 was somewhat redundant. CONCLUSION: The Assessment of Capacity for Myoelectric Control is a valid assessment that evaluates ability in using a prosthetic hand. Revision of the category 2 definition would improve the functioning of the rating scale.
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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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".