Orthotists' perspectives on the adjustment of 3D printed ankle foot orthoses: A mixed methods feasibility study
Bibliographic record
Abstract
BACKGROUND: An important part of the orthotic treatment process includes performing adjustments to the shape or design of the orthosis to improve its fit and function. However, the ability to adjust 3D printed (3DP) materials is not well understood. OBJECTIVES: (1) To evaluate the usability of completing adjustments on 3DP ankle foot orthoses (AFOs) vs. traditionally-fabricated AFOs, and (2) to explore orthotists' perspectives on the advantages, disadvantages, and similarities of adjusting 3DP materials and identify potential solutions for disadvantages. STUDY DESIGN: Mixed-methods cross-sectional study. METHODS: Ten participating certified orthotists performed a sequence of predetermined adjustment tasks on 3DP AFOs. The Single Ease Question and the System Usability Scale (SUS) were compared between traditional vs. 3DP AFO adjustments. Semistructured interviews were conducted, and a thematic analysis identified key themes. RESULTS: Single Ease Question scores were significantly lower for 3DP adjustments in 50% of tasks. The mean SUS total score was significantly lower ( p < 0.001) for tasks completed on 3DP AFOs compared with traditional AFOs. The thematic analysis identified challenges related to aesthetics, heating, grinding, brittleness, and timing of adjustments. Several similarities and some minor benefits were also noted. Despite challenges, orthotists demonstrated optimism about the 3DP material and proposed several solutions for improvement including optimizing techniques and introducing postprocessing. CONCLUSIONS: 3D printed orthoses were more difficult to adjust and had lower usability for adjustment compared with traditional ones. However, orthotists felt that they would be able to use the material in clinical practice if some of the proposed solutions were implemented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".