A scoping review of digital fabrication techniques applied to prosthetics & orthotics: Part 2 of 2—orthotics
Bibliographic record
Abstract
INTRODUCTION: Traditionally, orthosis manufacturing is time and labor-intensive. Digitalization of some of the fabrication process is already ubiquitous, yet extension across device types could reduce the burden of manual labor and advance automation to help unblock access to assistive technologies globally. It seems, however, that appropriately strong evidence is holding this back. This review looks to thoroughly examine the current state of evidence to make this clear. OBJECTIVES: To identify gaps in the literature that create barriers to decision-making on either appropriate uptake by clinical teams or setting research directions, by identifying what forms of evidence the current research literature provides to the orthotics community. STUDY DESIGN: Scoping literature review. METHODS: A comprehensive search was completed in the following databases: AMED, MEDLINE, EMBASE, Global Health Archive, CINAHL Plus, Cochrane Library, Web of Science, ACM, IEEE, and Engineering Village, resulting in 3487 articles to be screened. RESULTS: After screening, 121 lower limb orthosis, 104 upper limb orthosis, and 30 spinal orthosis articles were included in this review. For some areas such as CAD/CAM-produced insoles and spinal orthoses, the evidence base is strong. For most additive manufacture articles, long-term, larger-scale studies as well as research into training requirements are lacking. CONCLUSION: The advantages of digital fabrication technology that could streamline orthotic device production in many cases are still blocked by a lack of strong formal evidence, ie large longitudinal studies with a range of evaluation measures. Increased collaboration between clinicians, patient/service users, academia, and industry could be a route to addressing these gaps and creating a better pathway to market for new technologies.
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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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.030 | 0.030 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".