Evidence-Generated Sockets for Transtibial Prosthetic Limbs Compared With Conventional Computer-Aided Designs: A Multiple-Methods Study From the Patient’s Perspective
Bibliographic record
Abstract
Background: Personalized prosthetic socket design depends upon highly skilled prosthetists. They aim to balance functional human-prosthesis coupling with safe, comfortable load transmission from the prosthesis to the skeleton, through vulnerable skin and soft tissues. Both traditional plaster and computer-aided design and manufacturing (CAD/CAM) methods are iterative, and sharing knowledge is difficult. Evidence-generated (EG) sockets derived from past computer-aided socket design (CASD) records could provide a personalized starting point for limb fitting, potentially reducing time spent on basic design and enabling prosthetists to focus on more highly-skilled customization. Objective: This study aimed to assess the comfort of EG sockets, generated from past CASD records. Methods: A crossover trial compared EG sockets, derived from 163 previous transtibial devices, with conventional clinician-led CAD/CAM sockets. Noninferiority was assessed for the socket comfort score (SCS) outcome measure, and semistructured interviews provided in-depth user analysis. The setting was 3 UK National Health Service clinics, with 17 participants with 19 transtibial amputations. Results: EG sockets had no statistically significant difference in comfort compared with clinician-led control sockets (median SCS 8.6 for EG sockets and 8.8 for CAD/CAM controls; P=.43, effect size=0.05), but a lower variability in SCS across the group (95% CIs 8.0-9.0 for EG and 7.5-9.5 for CAD/CAM devices, respectively). Analysis of interviews revealed themes around fitting session experiences, similarities, and differences between the EG and CAD/CAM control sockets, and residual limb factors impacting perceptions of socket comfort. These provided insights into the participants' experience of the study and the value of expert prosthetist input in socket design. Conclusions: EG sockets demonstrated noninferiority to conventional clinical CASD practice in terms of socket comfort. Both quantitative and qualitative results indicated how clinician input remains essential and is valued by prosthesis users. Work is underway to incorporate the EG sockets into CASD software such that they can act as a digital starting point for modification by expert clinicians at fitting, potentially reducing time spent on basic design, enabling prosthetists to focus on more highly-skilled customization and co-design with their patients.
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 | 0.001 |
| 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".