Develop remote orthotic fabrication workflow using 3D modeling and 3D printing technology for carpometacarpal osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Carpometacarpal (CMC) osteoarthritis often requires orthotic intervention to reduce pain and improve function. Traditional orthosis fabrication typically demands in-person clinical visits, which can be a barrier to care. PURPOSE: To develop and validate a fully remote workflow for fabricating custom orthoses using mobile 3D scanning, web-based assessment, and 3D printing technologies for patients with CMC osteoarthritis. STUDY DESIGN: Pilot validation study. METHODS: A five-step workflow was created: (1) a web-based application ("Hand Scan") for assessing pain, sensory function, and range of motion; (2) mobile 3D hand scanning using smartphone cameras, with scans processed in Agisoft Metashape; (3) digital joint repositioning using finite element analysis software; (4) orthosis design using parametric modeling; and (5) fabrication using 3D printing with Orfit's low-temperature polycaprolactone filament. Five patients with CMC osteoarthritis were recruited. The application's usability was tested via cognitive interviews. Mobile scanning accuracy was validated against high-precision photogrammetry, and surface deviations of the 3D-printed orthoses were compared with traditional thermoplastic models. RESULTS: The Hand Scan app demonstrated strong content validity. Mobile scans showed a mean absolute deviation of 0.93 mm (SD = 0.61 mm). Joint repositioning yielded a mean deviation of 0.87 mm. The 3D-printed orthoses demonstrated a better fit than thermoplastic models, with a mean surface deviation of 0.95 mm compared to 1.96 mm. The maximum deviation was 3.17 mm for 3D-printed and 5.81 mm for traditional orthoses. CONCLUSIONS: This remote orthotic fabrication workflow is accurate, feasible, and clinically applicable. It supports personalized orthosis design while reducing the need for in-person visits. The workflow has strong potential for telehealth and remote hand therapy services.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".