3D PRINTED COSMETIC COVERS FOR LOWER LIMB PROSTHETICS
Bibliographic record
Abstract
Cosmetic covers provide better aesthetic appearance and may facilitate increased acceptance of the prosthesis. Traditionally, cosmetic covers aimed to achieve an aesthetic and realistic appearance; through time, a fresh perspective has developed on cosmetic covers where traditional/realistic covers evolved into a custom-made product, which truly promotes the patient’s personality. The objectives of the study were to gather information from five well-known companies in the cosmetic cover industry (UNYQ, Limb-Art, Alleles, WillowWood and Aqua-Leg), analyse and compare their design elements using the Pugh matrix, and suggest a novel design using the best performing criteria of each design. The overall results of the Pugh matrix revealed the opportunity of a new design with improved design elements mainly in terms of “cover fit/aesthetics”, “ease of attachment” and “practicality”. The novel design had a vast difference in total score from the second-best design, revealing the improvement possibilities such cover design may have in the future. Although the study successfully presented a theoretical novel design, it was based on data found through literature and web resources, making the overall results of the study more objective rather than subjective. Future research is encouraged to be conducted based on a more subjective approach towards cosmetic covers. Article PDF Link: https://jps.library.utoronto.ca/index.php/cpoj/article/view/42176/32350 How To Cite: Efstathiou K, McGarry A. 3D printed cosmetic covers for lower limb prosthetics. Canadian Prosthetics & Orthotics Journal. 2023; Volume 6, Issue 2, No.8. https://doi.org/10.33137/cpoj.v6i2.42176 Corresponding Author: Anthony McGarry, PhDDepartment of Biomedical engineering, University of Strathclyde, Glasgow, UK.E-Mail: anthony.mcgarry@strath.ac.ukORCID ID: https://orcid.org/0000-0002-0738-5906
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".