Understanding the Perceptions and Performance of Transtibial Prosthetic Covers: Perspectives and Preferences of Individuals With Lower Limb Absence, Prosthetists, and Technicians
Bibliographic record
Abstract
ABSTRACT Background The aesthetics and function of transtibial prosthetic covers can impact the functional abilities and activities that people with lower limb absence participate in. However, little is documented about how these covers are perceived by both people with lower limb absence and the prosthetic clinicians and technicians who work with them. Objective This study aimed to address this gap by exploring the performance and perceptions of transtibial covers from the perspective of those with lower limb absence (users), and prosthetic clinicians and technicians (professionals). Study Design We used a mixed methods design consisting of generative design sessions and a survey. Methods Twelve users and six professionals participated in the generative design sessions that explored cover use, drivers of cover selection, and needs and wants in cover design. Surveys, completed by 26 professionals and 33 users, expanded on these findings by examining individuals’ satisfaction with covers, important characteristics of covers, and challenges with current cover designs. Results Findings highlight desired features, limitations, and unmet needs with existing cover designs. Users’ suggestions focused on enhanced durability, functionality, and aesthetics and included a desire for cover designs that could prioritize their personal narrative and adapt to their varied daily lives. Users had diverse opinions about whether they wanted their prosthesis to stand out as a unique expression of themselves or have it match their sound limb as much as possible. Professionals’ suggestions for covers related to production issues such as using more durable materials, decreasing fabrication time, and having designs that are easier to adjust or repair. Conclusions Insights can be used to improve prosthetic cover designs, ideally supporting users to tell the stories they want to tell, while streamlining clinical workflows. Clinical Relevance Statement By understanding the unmet needs of people who use transtibial prosthetic covers as well as the clinicians and technicians who supply them, we can better design transtibial covers that meet the demands of the people who will wear or provide them.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".