Biomechanical Analysis of Vertebral Derotation Techniques for the Surgical Correction of Thoracic Scoliosis
Bibliographic record
Abstract
STUDY DESIGN: Biomechanical analysis of vertebral derotation techniques for the surgical correction of thoracic scoliosis. OBJECTIVE: To model and analyze vertebral derotation maneuvers biomechanically to maximize the tridimensional correction of scoliosis and minimize the implant-vertebra forces. SUMMARY OF BACKGROUND DATA: Vertebral derotation techniques were recently developed to improve the correction of scoliotic deformities in the transverse plane. Those techniques consist in applying a combination of moments and forces using a vertebral derotation device, cohesively linked to the thoracic apical pedicle screws, to derotate the spine and the rib cage. However, many variations of the technique exist and the correction mechanisms are not fully understood to achieve an optimal correction of scoliosis. METHODS: A biomechanical model was developed to simulate the instrumentation surgery numerically of 4 Lenke type 1 patients with scoliosis, instrumented using a vertebral derotation device and vertebral derotation maneuvers as major correction technique. Then, for each case, 32 additional instrumentation surgical procedures were simulated to better understand the biomechanics of the vertebral derotation technique, varying the implant type and density, the number of derotation levels, the vertebral derotation angle and the posteriorly oriented force applied during the maneuver. RESULTS: On average, among 32 additional simulations, there was an important variability of the resulting apical vertebral rotation (15°) and the mean resultant implant-vertebra force (205 N) but little variability for the main thoracic Cobb angle (6°) and the thoracic kyphosis (4°). The implant type, the implant density and the vertebral derotation angle were the parameters that most influenced the correction of scoliosis. The correction in the coronal and transverse planes was improved using monoaxial pedicle screw density of 2 and a bilateral vertebral derotation maneuver on 3 levels at the apex of the thoracic curve, with an extra 15° applied on the vertebral derotation device. When reducing the implant density by 50%, it was possible to reduce the mean implant-vertebra forces while keeping a good correction. CONCLUSION: Biomechanically, it is possible to significantly improve the correction of thoracic scoliotic deformities, particularly in the transverse plane, when using vertebral derotation maneuvers.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".