Comparing Prefabricated and 3D-Printed Foot Orthoses for the Management of Flat Foot Condition
Bibliographic record
Abstract
OBJECTIVE: The aim of the study is to investigate the short-term effects of 3D-printed and prefabricated foot orthoses on the management of flat feet. DESIGN: In this single-blinded study, 63 patients with flat feet were enrolled via convenience sampling. They were randomly assigned to the control and experimental groups, receiving prefabricated and customized 3D-printed foot orthoses, respectively. The assessment tools included a visual analog scale and a modified Quebec User Evaluation of Satisfaction with Assistive Technology questionnaire. The patients scored their pain at weeks 0 and 4 using the visual analog scale. At the end of week 4, patients completed the modified version of the Quebec User Evaluation of Satisfaction with Assistive Technology questionnaire to record their satisfaction with the orthosis. RESULTS: Visual analog scale scores at week 0 for the two groups were not statistically significant ( P > 0.05). At week 4, the visual analog scale scores of the experimental group reduced significantly ( P < 0.001), whereas the visual analog scale scores of the control group remained statistically insignificant ( P > 0.05). Modified Quebec User Evaluation of Satisfaction with Assistive Technology questionnaire analysis revealed higher satisfaction with orthosis comfort and effectiveness in the experimental group than in the control, while factors like orthosis dimensions, weight, and durability did not differ significantly between groups. CONCLUSIONS: Customized 3D-printed orthoses effectively reduced pain and enhanced patient satisfaction with comfort and effectiveness in 4 wks.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".