Relation between capacity and performance in paediatric upper limb prosthesis users
Bibliographic record
Abstract
BACKGROUND: The International Classification of functioning, disability and health refers capacity to what an individual can do in a standardised environment and describes performance as what an individual really does and whether the individual encounters any difficulty in the real-life environment. Measures of capacity and performance can help to determine if there is any gap between them that may restrict participation. The aim of this study was to explore the relationship between capacity scores obtained in a standardised clinical setting and proportional ease of performance obtained from a real-life environment. METHODS: The Assessment of Capacity for Myoelectric Control and the Prosthetic Upper Extremity Functional Index were used to assess capacity and performance in 62 prosthetic users (age 3-17). Spearman coefficient and generalised linear model were used to examine the association between these measures. RESULTS: A strong correlation (Spearman = 0.75) was found between the capacity scores and the ease of performance. In both unadjusted and adjusted models, capacity was significantly associated with proportional ease of performance. The adjusted model showed that, by 1 unit increase in the Assessment of Capacity for Myoelectric Control score, the ratio of proportional ease of performance increases by 45%. CONCLUSION: This implies that Assessment of Capacity for Myoelectric Control can be a predictor for ease of performance in real-life environment. Clinical relevance The ACMC scores may serve as an indicator to predict the difficulties that the children may encounter in their home environment. This prediction can help the clinician to make decisions, such that if the child requires more control training or is ready to move on to learn more complex tasks.
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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 | 0.001 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".