A comparison of the University of New Brunswick Test of Prosthetic Function and the Assessment of Capacity for Myoelectric Control.
Bibliographic record
Abstract
BACKGROUND: Valid outcome measures are required for gathering evidence on when, how and using which prosthetic components to start prosthetic fitting to children with congenital upper limb deficiencies and those following acquired upper limb amputation. We have been using the University of New Brunswick Test of Prosthetic Function (UNB, which measures skill and spontaneity of prosthetic use) in our country since 1996, and the Assessment of Capacity for Myoelectric Control (ACMC, which was developed for persons using a myoelectric prosthesis) since 2008. AIM: We wanted to explore whether the UNB and the ACMC measure the same construct (i.e., assess convergent validity), and whether the ACMC can also be used for assessing children and adolescents who use a body-powered upper-limb prosthesis. DESIGN: Observational. SETTING: Inpatient. POPULATION: All the 19 children and adolescents who were visiting the outpatient clinic for rehabilitation of children with upper limb deficiencies and amputations at our institute from January 2010 to December 2011 and had a myoelectric (15 participants) or body-powered prosthesis (4 participants). METHODS: The participants were assessed by the age-appropriate UNB subtest; 60 assessments were performed in total. Two tests (moving a plastic glass half-full with water from table to the sink, and tying apron at the back) were added to obtain the ACMC scores. RESULTS: Simple and autocorrelation-adjusted correlation and regression analyses demonstrated that ACMC score is highly positively correlated with UNB spontaneity and skill score in children and adolescents who use a myoelectric prosthesis. Neither of the two associations could be observed in children and adolescents who use a body-powered prosthesis. CONCLUSION: The results suggest that both tests can be used for assessing children and adolescents who use a myolectric prosthesis, but only the UNB appears to be appropriate for those who use a body-powered prosthesis. CLINICAL REHABILITATION IMPACT: This small study indicates that either the ACMC or the UNB are applicable for assessing children and adolescents who use a myolectric upper-limb prosthesis because of congenital upper limb deficiencies or acquired upper limb amputation, but only the UNB seems to be applicable for those children or adolescents who use a body-powered prosthesis.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".