A scoping review of digital fabrication techniques applied to prosthetics and orthotics: Part 1 of 2—Prosthetics
Bibliographic record
Abstract
BACKGROUND: Traditionally, the manufacture of prostheses is time-consuming and labor-intensive. One possible route to improving access and quality of these devices is the digitalizing of the fabrication process, which may reduce the burden of manual labor and bring the potential for automation that could help unblock access to assistive technologies globally. OBJECTIVES: To identify where there are gaps in the literature that are creating barriers to decision-making on either appropriate uptake by clinical teams or on the needed next steps in research that mean these technologies can continue on a pathway to maturity. STUDY DESIGN: Scoping literature review. METHODS: A comprehensive search was completed in the following databases: Allied and Complementary Medicine Database, MEDLINE, Embase, Global Health Archive, CINAHL Plus, Cochrane Library, Web of Science, Association for Computing Machinery, Institute of Electrical and Electronics Engineers, and Engineering Village, resulting in 3487 articles to be screened. RESULTS: After screening, 130 lower limb prosthetic articles and 117 upper limb prosthetic articles were included in this review. Multiple limitations in the literature were identified, particularly a lack of long-term, larger-scale studies; research into the training requirements for these technologies and the necessary rectification processes; and a high range of variance of production workflows and materials which makes drawing conclusions difficult. CONCLUSIONS: These limitations create a barrier to adequate evidence-based decision-making for clinicians, technology developers, and wider policymakers. Increased collaboration between academia, industry, and clinical teams across more of the pathway to market for new technologies could be a route to addressing these gaps.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.032 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".