Immediate correction required to expect a long-term effectiveness of a brace treatment: a biomechanical insight
Bibliographic record
Abstract
Immediate in-brace correction has often been deemed as fundamental to long-term brace effectiveness but the biomechanical rational is unclear and unproven. To biomechanically study how the immediate in-brace correction of the scoliotic curves is affecting the mechanisms involved in the long term correction of the spine. The three-dimensional geometry of 30 patients was acquired using multi-view radiographic reconstruction and surface topography techniques. A finite element model of the trunk and a parametric brace model were created. For each case, two spinal stiffnesses (flexible, stiff) were tested. Installation of the brace was simulated. Using an experimental design framework including thirteen design factors, 768 braces were tested for each patient (total of 69120 tested braces). Immediate in-brace correction of the coronal Cobb angles and loads acting on the growth plates of the apical vertebrae were computed and analyzed. Immediate correction of coronal curves and corresponding bending loads on the apical vertebrae were linearly correlated (mean R2 = 0.86). 10% to 99% of immediate correction was necessary to nullify the asymmetric loads, with an average of 49% (flexible spine model) and 35% (stiff spine model).
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".