DO MULTI-GRIP HANDS INCREASE FUNCTION AND PATIENT SATISFACTION WHEN COMPARED TO TRADITIONAL MYOELECTRIC HANDS?
Bibliographic record
Abstract
INTRODUCTION Myoelectric hands progressed from single grip hands (traditional myoelectric devices (TH)) to be multi-grip hands (MGH) which are hypothesized to bring more degrees of freedom, greater range of motion and improved grasping capabilities1,2. Their impact on patients’ lives has been documented in only a few case studies. The Strategic Consortium for Upper Limb Prosthetic Technologies (SCULPT) aims to assess the potential benefits MGH with respect to function and patient satisfaction compared to TH systems. Abstract PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/32049/24463 How to cite: Popovic I, Cutti A, Ryan T, Schaefer M, Andres E, Wuestefeld D, Winkler C, Baun K, Bischof B, Braatz F, Miguelez J, Conyers D, Hahn A. DO MULTI-GRIP HANDS INCREASE FUNCTION AND PATIENT SATISFACTION WHEN COMPARED TO TRADITIONAL MYOELECTRIC HANDS? 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.v1i2.32049 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.024 | 0.002 |
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".