Iterative codesign and testing of a novel dressing glove for epidermolysis bullosa
Bibliographic record
Abstract
OBJECTIVE: Recessive dystrophic epidermolysis bullosa (RDEB) is a rare genetic skin disorder which requires intensive hand therapy to delay fusion of the digits. Existing dressings do not conform to the complex structure of the hand and are applied in patches held with additional bandages, leading to an occlusive environment. The aim was to co-design with patients a dressing glove based on their user experiences and needs. METHOD: Qualitative interviews and focus groups with children and adults with RDEB, and their carers, were conducted. Iterative feedback of design cues, bench and surrogate testing of materials and prototype refinement were achieved through collaborative codesign with patients, carers, clinicians and manufacturers. RESULTS: Thematic analysis generated eight user needs and corresponding design cues, addressing issues of absorbency, adherence, comfort, adaptability, ease of application and removal, breathability, protection, and hand hygiene. A prototype was selected for proof of concept testing. CONCLUSION: This novel dressing glove design met the patient's requirements for a dressing, which conformed to the hand structure and sat in the web spaces to keep fingers separated. Proof of concept testing has since been undertaken with patients to determine performance, value for money and whether further developments are required.
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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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".