USER EXPERIENCES WITH TRADITIONAL AND 3D-PRINTED UPPER EXTREMITY PROSTHESES, DEVELOPMENT OF A COMPREHENSIVE SURVEY INSTRUMENT
Bibliographic record
Abstract
INTRODUCTION Additive Manufacturing (AM), colloquial known as 3D-printing, has been deemed capable to revolutionize a great number of industries, including the Health Care industry.1 In the field of upper limb prosthetics, it has been attempted to leverage the potential advantages of AM, such as crowd based design optimization, infrastructure independent fabrication, and economical material use, in the interest of providing low-cost, readily available devices to recipients whose needs were only insufficiently met by traditional approaches of device prescription and fitting. While the popular media has been quick to emphasize the potential – perceived or real – of 3D printed prostheses, clinicians have generally been less euphoric and the base of scientific evidence on questions related to these applications has been small.2 As with most research endeavors in prosthetics and orthotics, recruiting sufficient sample sizes to allow solid conclusions is a perennial challenge also in this sub-field. As a consequence, the effectiveness of the many 3D-printed upper limb devices made by volunteers of the E-nable community (Fig.1) is yet to be determined. Self-reported outcome assessment tools can somewhat mitigate the issue of low sample sizes. However, none have been applied to a wider range of device classes, to allow comparative analyses across those. We describe the development and preliminary testing of an online based survey tool to generate comparison outcome data for a wide variety of upper limb prosthetics devices, including varieties that are 3D-printed by hobbyists. Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32009/24428 How to cite: Mankoff J, Savage S, Eckert S, Ngo C, Fiedler G. USER EXPERIENCES WITH TRADITIONAL AND 3D-PRINTED UPPER EXTREMITY PROSTHESES, DEVELOPMENT OF A COMPREHENSIVE SURVEY INSTRUMENT. CANADIAN PROSTHETICS & ORTHOTICS JOURNAL, VOLUME 1, ISSUE 2, 2018; ABSTRACT, POSTER PRESENTATION AT THE AOPA’S 101ST NATIONAL ASSEMBLY, SEPT. 26-29, VANCOUVER, CANADA, 2018. DOI: https://doi.org/10.33137/cpoj.v1i2.32009 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 | 0.020 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".