Biomechanical modeling of brace design.
Bibliographic record
Abstract
OBJECTIVE: To study the biomechanical effectiveness of brace design parameters in right thoracic idiopathic scoliosis. METHODS: A finite element model (FEM) of the spine, rib cage, pelvis and abdomen was adapted to the geometry of 8 patients with right-thoracic idiopathic scoliosis using a multi-view radiographic reconstruction technique. A detailed parametric FEM of a thoraco-lumbo-sacral orthosis and a Box, Hunter & Hunter experimental design method were used to analyze the contribution of brace design parameters (brace size, number of straps, strap tension, position of the thoracic pad, lordosis reduction design) and of patient's spine stiffness. RESULTS: The mean Cobb angle correction of the thoracic curve was 5.1 degrees (0 degrees to 16 degrees). The most influential parameters were, in descending order, the strap tension, lordosis reduction design and spine stiffness. Their effects are independent and remain weak (-3 degrees when strap tension increases from 20 N to 60 N). Changing the position of the thoracic pad (slightly above or below the apex) doesn't have a significant effect. No significant correction of the axial rotation and rib hump was obtained. DISCUSSION & CONCLUSION: Frontal curve correction varied significantly, which justifies the need for an adequate adjustment of the brace. A more efficient design for the correction of transverse deformities remains to be found. The "active" correction component by the muscles was not included, but one can anticipate that its action would be concurrent to the passive brace mechanisms, enabling supplementary correction. A new tool simulating brace treatment has been developed, which allows rational design of braces.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".