Comparison of Socket Geometry, Socket Comfort, and Patient Experience between Manually- and Digitally-Designed Prosthetic Sockets for Lower-Limb Amputees: A Feasibility Study
Bibliographic record
Abstract
Prosthetic socket manufacturing is experiencing a revolutionary shift towards using digital methods, such as 3D scanning and 3D printing. However, using digital methods requires the clinician to transfer their skills from making sockets by hand to making sockets with a computer. This shift in practice may change the socket geometry and fit; however, to what extent is unknown. Thus, the aim of this study was to explore the feasibility of analyzing geometric and clinical differences between digitally- and manually-designed sockets. Nine adult inpatients with below-knee amputation were recruited. Two sockets were 3D printed for each participant from 3D socket models that were developed from: (1) 3D scanning a manually-modified hand-casted positive mold of the residuum; and (2) a digitally-modified 3D scan of the residuum. Manual and digital procedures were compared for three measures: final socket geometry, the Socket Comfort Score, and a patient experience survey. Feasibility data were collected to measure protocol implementation fidelity to inform a future larger study. These data revealed that 89% of participants followed the intended protocol, no participants dropped out, and only one adverse event was report. As no significant geometric differences were found and participants experienced similar comfort scores between manually- and digitally-designed sockets, study feasibility was determined to be successful. Thus, a randomized control trial study will be conducted to draw statistically relevant conclusions from these outcome measures that may provide meaningful information for improving digital design procedures.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".