Polymer-Based Additive Manufacturing for Orthotic and Prosthetic Devices: Industry Outlook in Canada
Bibliographic record
Abstract
The conventional manufacturing methods for fabricating orthotic and prosthetic (O&P) devices have been in practice for a very long time. Recently, O&P service providers have started exploring different advanced manufacturing techniques. The objective of this paper is to perform a mini review on recent progress in the use of polymer-based additive manufacturing (AM) for O&P devices and to gather insights from the O&P professionals on the current practices and technologies and on the prospect of using AM techniques in this field. In our study, first, scientific articles on AM for O&P devices were studied. Then, twenty-two (22) interviews were conducted with O&P professionals from Canada. The primary focus was on five key areas: cost, material, design and fabrication efficiency, structural strength, functionality, and patient satisfaction. The cost of manufacturing the O&P devices using AM techniques is lower as compared to the conventional methods. O&P professionals expressed their concern over the materials and structural strength of the 3D-printed prosthetic devices. Published articles report comparable functionality and patient satisfaction for both O&P devices. AM also greatly improves design and fabrication efficiency. However, due to a lack of qualification standards for 3D printed O&P devices, 3D printing is being embraced more slowly in the O&P business than in other industries.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".