The use of a low cost 3D scanning and printing tool in the manufacture of custom-made foot orthoses: a preliminary study
Bibliographic record
Abstract
BACKGROUND: Custom foot orthoses are currently recognized as the gold standard for treatment of foot and lower limb pathology. While foam and plaster casting methods are most widely used in clinical practice, technology has emerged, permitting the use of 3D scanning, computer aided design (CAD) and computer aided manufacturing (CAM) for fabrication of foot molds and custom foot orthotic components. Adoption of 3D printing, as a form of CAM, requires further investigation for use as a clinical tool.This study provides a preliminary description of a new method to manufacture foot orthoses using a novel 3D scanner and printer and compare gait kinematic outputs from shod and traditional plaster casted orthotics. FINDINGS: One participant (male, 25 years) was included with no lower extremity injuries. Foot molds were created from both plaster casting and 3D scanning/printing methods. Custom foot orthoses were then fabricated from each mold. Lower body plug-in-gait with the Oxford Foot Model on the right foot was collected for both orthotic and control (shod) conditions. The medial longitudinal arch was measured using arch height index (AHI) where a decrease in AHI represented a drop in arch height. The lowest AHI was 21.2 mm in the running shoes, followed by 21.4 mm wearing the orthoses made using 3D scanning and printing, with the highest AHI of 22.0 mm while the participant wore the plaster casted orthoses. CONCLUSION: This preliminary study demonstrated a small increase in AHI with the 3D printing orthotic compared to the shod condition. A larger sample size may demonstrate significant patterns for the tested conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".