Anterior Vertebral Body Growth Modulation
Bibliographic record
Abstract
STUDY DESIGN: Numerical planning and simulation of immediate and after 2 years growth modulation effects of anterior vertebral body growth modulation (AVBGM). OBJECTIVE: The objective was to evaluate the planning tool predictive capability for immediate, 1-year, and 2-year postoperative correction and biomechanical effect on growth modulation over time. SUMMARY OF BACKGROUND DATA: AVBGM is used to treat pediatric scoliotic patients with remaining growth potential. A planning tool based on a finite element model (FEM) of pediatric scoliosis integrating growth was previously developed to simulate AVBGM installation and growth modulation effect. METHODS: Forty-five patients to be instrumented with AVBGM were recruited. A patient-specific FEM was preoperatively generated using a 3D reconstruction obtained from biplanar radiographs. The FEM was used to assess different instrumentation configurations. The strategy offering the optimal 2-year postoperative correction was selected for surgery. Simulated 3D correction indices, as well as stresses applied on vertebral epiphyseal growth plates, intervertebral discs, and instrumentation, were computed. RESULTS: On average, six configurations per case were tested. Immediate, 1-year, and 2-year postoperative 3D correction indices were predicted within 4° of that of actual results in coronal plane, whereas it was <0.8 cm (±2%) for spinal height. Immediate postoperative correction was of 40%, whereas an additional correction of respectively 13% and 3% occurred at 1- and 2 year postoperative. The convex/concave side computed forces difference at the apical level following AVBGM installation was decreased by 39% on growth plates and 46% on intervertebral discs. CONCLUSION: This study demonstrates the FEM clinical usefulness to rationalize surgical planning by providing clinically relevant correction predictions. The AVBGM biomechanical effect on growth modulation over time seemed to be maximized during the first year following the installation. LEVEL OF EVIDENCE: 3.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".