Immediate Outcomes and Benefits of 3D Printed Braces for the Treatment of Adolescent Idiopathic Scoliosis
Bibliographic record
Abstract
Spinal bracing is a proven effective treatment for children with adolescent idiopathic scoliosis (AIS). Four factors have been reported to affect brace treatment outcome including (1) growth or curve-based risk, (2) the in-brace correction, (3) the brace wear quantity, and (4) the brace wear quality. The in-brace correction is impacted by spinal flexibility. The quality of brace design also affects the in-brace correction and comfort which indirectly affects the brace wear quantity and quality. A traditional polypropylene spinal brace is bulky and uncomfortable, and its manufacturing process is labor intensive. As 3D printing technology becomes more common and advanced, there is a potential to manufacture spinal braces using 3D printing technology. The objectives of this paper were to report the immediate effectiveness and benefits in using 3D printed brace to treat children with AIS. Six children with AIS (5F, 1M; 12.9 ± 1.4 years old; Cobb angle: 26° ± 7°), who were new to brace treatment, were recruited. Spinal flexibility and pressure pad locations were acquired using ultrasound assisted method to ensure braces were designed properly. To manufacture the braces, all participants were scanned by a handheld 3D scanner to obtain their body shapes. The 3D braces were then printed with Nylon 12 material. The average in-brace Cobb angle correction was 10 ± 4° (41 ± 18% correction). The 3D brace was 33% thinner, 26% lighter, 37% lower cost and required 3.7 h less labor time to manufacture when compared with the standard polypropylene brace. As a conclusion, the 3D printed brace had good immediate treatment effectiveness, but the long-time effect is still required time to explore.
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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".