FACTOR ANALYSIS OF UPPER LIMB PROSTHETIC ACCEPTANCE FROM RETROSPECTIVE PROSTHETIC CLINICIAN SURVEY
Bibliographic record
Abstract
INTRODUCTION Upper limb prosthetic acceptance seems to be relatively unchanged from 1958 where it was measured to be 75% for transradial, 61% for transhumeral, and 35% for shoulder disarticulation levels. A practitioner survey from 2013 by the author found this to be largely unchanged at 79.6%, 57.8%, and 32.8% respectively. An upper limb meta-analysis showed that the most significant factors affecting prosthetic rejection using a median rating were function, comfort, ease of use, weight, heat, lack of sensory feedback, inconvenience, lifestyle, dissatisfaction with technology, irritation, and availability of services. An earlier survey by the author condensed these factors of rejection to amputation level, functional advantage, and comfort, and included confidence of the prosthetist, availability of therapy, and support of the patient context. Also it was speculated that the value of factors influencing rejection of prostheses may not be simply the converse of those accepting the prosthesis but different scales. Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32045/24459 How to cite: Stark G E. FACTOR ANALYSIS OF UPPER LIMB PROSTHETIC ACCEPTANCE FROM RETROSPECTIVE PROSTHETIC CLINICIAN SURVEY. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, ORAL PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32045 Abstracts were Peer-reviewed by the American Orthotic Prosthetic Association (AOPA) 101st National Assembly Scientific Committee. http://www.aopanet.org/
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".