Preliminary characterization of rectification for transradial prosthetic sockets
Bibliographic record
Abstract
Achieving proper socket fit is crucial for the effective use of a prosthesis. However, digital socket design lacks standardization and presents a steep learning curve for prosthetists. While research has focused on digital socket design for the lower-limb population, there is a research gap in upper-limb socket design. This study aimed to characterize the design (rectification) process for the transradial socket, specifically the three-quarter Northwestern-style design, towards the development of a more systematic, data-driven socket design approach. Fourteen (n = 14) pairs of unrectified and rectified plaster models were compared. Six common rectification zones were identified through shape analysis, with zones of plaster addition being the most prominent in terms of volume and surface area. A novel 3D vector mapping technique was employed, which revealed that most of the shape changes occurred in the anterior-posterior and proximal-distal directions. Overall, the interquartile range of each rectification zone demonstrated reasonable consistency in terms of volume, surface deviation, and 3D vector representation. The initial findings from this study support the potential for quantitively modelling the transradial socket design process. This opens the door for developing tools for categorizing and predicting socket designs across diverse populations through the application of techniques such as machine learning.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".