Finite element simulation of growth modulation during brace treatment of adolescent idiopathic scoliosis
Bibliographic record
Abstract
Adolescent idiopathic scoliosis (AIS) is a spine deformity whose progression during growth is affected by asymmetrical loads acting on the spine. The conservative brace treatment aims to limit the deformity's progression until the end of skeletal growth. This study's objective was to develop a patient-specific finite element model (FEM) simulating immediate in-brace (IB) correction and subsequent growth modulation over 2 years of treatment. Thirty-five retrospective AIS cases with documented correction over 2 years were analyzed. For each case, a patient-specific FEM was built and IB correction was simulated. Vertebral growth and its modulation were modeled using simulated pressures on epiphyseal vertebral growth plates, including a compliance factor representing the recorded brace wear. The simulated Cobb angles, thoracic kyphosis, lumbar lordosis, and apical vertebral rotation were compared with the actual measurements immediately IB and out-of-brace (OOB) at the 2-year follow-up. Treatment outcomes according to simulated compliance scenarios of no brace-wear versus full brace-wear were also computed. The average immediate IB difference between the simulated and actual Cobb angle was 4.9° (main thoracic [MT]) and 3.7° (thoraco-lumbar/lumbar [TL/L]). Two-year OOB, it was 5.6° (MT) and 5.4° (TL/L). The no brace-wear and full brace-wear compliance scenarios resulted respectively in 15/35 (43%) and 31/35 (89%) simulated spine deformities progressing by <5° over 2 years of treatment. Clinical significance: the FEM's ability to simulate the final correction with an accuracy on the order of the radiological measurements' interoperator reproducibility, combined with its sensitivity to brace-wear compliance, provides confidence in the model's predictions for a comparative context of use like improving a brace's design before its application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".