Consumer design priorities for upper limb prosthetics
Bibliographic record
Abstract
PURPOSE: To measure consumer satisfaction with upper limb prosthetics and provide an enumerated list of design priorities for future developments. METHODS: A self-administered, anonymous survey collected information on participant demographics, history of and goals for prosthesis use, satisfaction, and design priorities. The questionnaire was available online and in paper format and was distributed through healthcare providers, community support groups, and one prosthesis manufacturer; 242 participants of all ages and levels of upper limb absence completed the survey. RESULTS: Rates of rejection for myoelectric hands, passive hands, and body-powered hooks were 39%, 53%, and 50%, respectively. Prosthesis wearers were generally satisfied with their devices while prosthesis rejecters were dissatisfied. Reduced prosthesis weight emerged as the highest priority design concern of consumers. Lower cost ranked within the top five design priorities for adult wearers of all device types. Life-like appearance is a priority for passive/cosmetic prostheses, while improved harness comfort, wrist movement, grip control and strength are required for body-powered devices. Glove durability, lack of sensory feedback, and poor dexterity were also identified as design priorities for electric devices. CONCLUSIONS: Design priorities reflect consumer goals for prosthesis use and vary depending on the type of prosthesis used and age. Future design efforts should focus on the development of more light-weight, comfortable prostheses.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".