THE CASE FOR BROAD-RANGE OUTCOME ASSESSMENT ACROSS UPPER LIMB DEVICE CLASSES
Bibliographic record
Abstract
The emergence of 3D-printed upper limb prosthetic devices a couple of years ago, spearheaded substantially by the e-NABLE community (1, 2), has triggered a variety of reactions, ranging from euphoric press coverage predicting a new age of low-cost universally obtainable prosthetic solutions to anxious reluctance by clinicians fearing the demise of high-quality professional health care provision (3, 4). The circumstance that untrained volunteers produce e-NABLE devices on their hobby-grade 3D-printers (5) was both hailed as a revolutionary paradigm shift suited to address a host of current challenges in health care economics, and derided as inappropriate intrusion into long-standing training and certification standards of a well-regulated profession. That many of the early generation e-NABLE devices targeted young patients with partial hand amputation (6) was interpreted by proponents as finally offering this neglected population long-desired solutions, whereas skeptics felt that many of the recipients of such devices would traditionally have been deemed to have a residual functional enough to be a contra-indication for a prosthesis (7).Article PDF file: https://jps.library.utoronto.ca/index.php/cpoj/article/view/29970/22869 How to cite: Fiedler G, Savage S, Schull J, Mankoff J. The Case For Broad-Range Outcome Assessment Across Upper Limb Device Classes. Canadian Prosthetics & Orthotics Journal. Volume1, Issue1, No4, 2018. DOI: https://doi.org/10.33137/cpoj.v1i1.29970
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".