Immediate Correction of Idiopathic Scoliosis With Nighttime Braces Created by a Fully Automated Generative Design Algorithm
Bibliographic record
Abstract
STUDY DESIGN: Single-center, double-blinded, prospective crossover randomized controlled trial. OBJECTIVE: To clinically validate the efficacy of nighttime braces designed automatically by a generative design algorithm to treat idiopathic scoliosis (IS). The tested hypothesis was the clinical equivalence of immediate in-brace correction for the new automatically generated brace design versus a standard Providence-type brace. SUMMARY OF BACKGROUND DATA: Documented efficacy of brace treatment varies between centers, and depends on the empirical expertise of the treating orthotist. Our group previously developed a fully automated generative brace design algorithm that leverages a patient-specific finite-element model (FEM) to optimize brace geometry and correction before its fabrication. METHODS: Fifty-eight skeletally immature patients diagnosed with IS, aged between 10 and 16 years were recruited. All patients received both a nighttime brace automatically generated by the algorithm (test) and a Providence-type brace designed by an expert orthotist (control). Radiographs were taken for each patient with both braces in a randomized crossover approach to evaluate immediate in-brace correction. RESULTS: The targeted 55 patients (48 females, 7 males) completed the study. The immediate Cobb angle correction was 57% 19 (test) versus 58% 21 (control) for the main thoracic (MT) curve, whereas it was 89% 25 (test) versus 87% 28 (control) for the thoracolumbar/lumbar (TLL) spine. The immediate correction with the test brace was noninferior to that of the Control brace ( P 0.001). The order in which the braces were tested did not have a residual effect on the immediate correction. CONCLUSION: The fully automated generative brace design algorithm proves to be clinically relevant, allowing for immediate in-brace correction equivalent to that of braces designed by expert orthotists. Patient 2 years follow-up will continue. This method's integration could help design and rationalize the design of braces for the treatment of IS. LEVEL OF EVIDENCE: Level 2.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".