User perspectives of digital manufacturing for lower-limb prosthetic sockets
Bibliographic record
Abstract
BACKGROUND: There is growing interest to use digital technology (DT) for manufacturing lower-limb prosthetic sockets to improve efficiency and clinical outcomes. However, little is known about how lower-limb prosthesis users perceive DTs, such as 3D scanning and 3D printing. OBJECTIVES: This study aimed to provide an understanding of perceptions and experiences with DT for prosthetic socket manufacturing from the perspective of prosthesis users. STUDY DESIGN: A qualitative descriptive research study. METHODS: Nine lower-limb prosthesis users (mean age 56; 5 female; 4 male) participated in one-on-one semistructured telephone interviews. Inductive thematic analysis was performed to identify a codebook and emerging themes from the interview transcripts. RESULTS: Two major themes were identified: (1) expectations and prioritization of 3D printed socket usability and (2) facilitators and barriers to uptake of DT among patients. CONCLUSION: DT methods were found to be acceptable and feasible from a patient perspective, although technological advancements are still required, and real-time communication about the process may be vital for ensuring patient engagement. Consideration of these findings may improve patient satisfaction to emerging prosthesis treatment plans and ultimately support widespread adoption of DT as an additional tool for fabricating prosthetic sockets.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".