A qualitative study exploring healthcare professionals’ perceptions of lower limb 3D printed sockets
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to explore healthcare professionals' (HCPs) perceptions and experiences related to 3D scanning and 3D printing for fabricating lower limb prosthetic sockets. MATERIALS AND METHODS: This study used a qualitative descriptive approach. Participants were recruited through HCPs' professional associations, social media posts, and snowball sampling. Purposive sampling was used to attain variation in provider type. One-on-one telephone interviews were conducted using a semi-structured interview guide. Inductive thematic analysis was performed to identify the main themes. RESULTS: Three themes were identified: (1) 3D scanning of the residual limb for designing prosthetic sockets is perceived as clean, quick, and convenient; (2) concerns about the strength and safety of 3D printed sockets for long-term use; (3) Adoption of 3D scanning and 3D printing technology for fabricating prosthetic sockets. CONCLUSION: We identified perceived benefits and challenges with digital technologies for fabricating prosthetic sockets. To increase adoption, more research demonstrating its efficacy compared to conventional methods, increasing 3D printing material quality, and improving software training programs are needed.Implications for Rehabilitation3D printing and 3D scanning are emerging digital technologies that can be used as alternative methods for prosthetic socket manufacturing in the field of rehabilitation.Our research identified perceived benefits of using digital technologies for fabricating prosthetics sockets (3D scanning is perceived as clean, quick, and convenient) and perceived challenges (concerns about the strength and safety of 3D printed sockets for long-term use and a prolonged learning curve).To increase adoption of these digital technologies, more training should be provided to prosthetists and support provided to integrate new processes into staff workloads.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".