Qualitative needs assessment for the development of a smart thumb prosthesis
Bibliographic record
Abstract
PURPOSE: To critically explore experiences following thumb amputation and delineate elements of an ideal thumb prosthesis from the end user perspective. METHODS: A qualitative study was undertaken with end user stakeholder groups, which included persons with a thumb amputation, rehabilitation professionals, and prosthetists. Analysis proceeded in line with conventional content analysis. RESULTS: Six patients with traumatic thumb amputation and eight healthcare providers (HCPs) were interviewed. Six themes were identified. The first theme discussed the impact of losing a thumb upon function, occupational activities, and mental wellbeing. The second theme reflected the idiosyncratic nature of thumb amputees, including their goals and nature of injury. The third theme stressed the costs associated with obtaining a thumb prosthesis. The fourth theme explored patient frustration and causes of device abandonment. Theme five summarized opinions on currently available thumb prostheses, and theme seven was the ideal design for a thumb prosthetic, including sensory elements and materials. CONCLUSIONS: Representative data from stakeholders mapped the current status of thumb prostheses. Preferences for an ideal thumb prosthesis included a simple, durable design with the ability to oppose, grasp, and sense pressure. Affordable cost and ease of fit emerged as systemic objectives.
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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 | 0.042 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".