Personalized Biomechanical Modeling of Boston Brace Treatment in Idiopathic Scoliosis
Bibliographic record
Abstract
The aim of this study was to describe how the Boston brace modify the scoliotic curvatures using a finite element (FE) model and experimental measurements. The experimental protocol, applied on 12 scoliotic girls, was composed of the pressure measurement at the brace-torso interface followed by two radiographic acquisitions of the patient's torso with and without brace. A 3D FE model of the trunk was built for each unbraced patient. The brace treatment was represented by two different modeling approaches: 1) using equivalent forces calculated from the measured pressures; 2) by an explicit personalized FE model of the brace (hexahedral elements) and its interface with the torso (contact elements). In the first model, measured brace forces less than 40N and up to 113N induced respectively less than 21% and up to 87% of real correction. Thoracic forces induced the main correction, affecting partially both lumbar and thoracic curves, in agreement with the literature. In the second model, the brace closing reduced the curves up to 35% of real correction. Contact reaction forces (16-79N) were similar to real brace forces (11-72N). The results suggested that other mechanisms than brace pads contribute to the equilibrium of the patients. Postural control by the muscular system remains a problem to address in a future study. The second model represented more realistically the load transfer from the brace to the spine than external forces application. With such model, it is expected to predict the effect of a brace before its design and manufacturing, and also to improve its design.
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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 | 0.000 |
| 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 | 0.001 | 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".