The Overlay, a New Solution for Volume Variations in the Residual Limb for Individuals with a Transtibial Amputation
Bibliographic record
Abstract
Background: The company Ethnocare has developed the Overlay, a new pneumatic solution for managing volumetric variations (VVs) of the residual limb (RL) in transtibial amputees (TTAs), which improves socket fitting. However, the impact of the Overlay during functional tasks and on the comfort and pain felt in the RL is unknown. Methods: 8 TTAs participated in two evaluations, separated by two weeks. We measured compensatory strategies (CS) using spatio-temporal parameters and three-dimensional lower limb kinematics and kinetics during gait and sit-to-stand (STS) tasks. During each visit, the participant carried out our protocol while wearing the Overlay and prosthetic folds (PFs), the most common solution to VV. Between each task, comfort and pain felt were assessed using visual analog scales. Results: While walking, the cadence with the Overlay was 105 steps/min, while it was 101 steps/min with PFs (p = 0.021). During 35% and 55% of the STS cycle, less hip flexion was observed while wearing the Overlay compared to PFs (p = 0.004). We found asymmetry coefficients of 13.9% with the Overlay and 17% with PFs during the STS (p = 0.016) task. Pain (p = 0.031), comfort (p = 0.017), and satisfaction (p = 0.041) were better with the Overlay during the second visit. Conclusion: The Overlay’s impact is similar to PFs’ but provides less pain and better comfort.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".