Simulations of Rib Cage Surgery for the Management of Scoliotic Deformities
Bibliographic record
Abstract
Costoplasties are surgical options to treat rib cage deformities. The main concern of rib resections is often for the cosmetic improvement of the back shape of the patient. Other experimental and clinical studies have shown that a costoplasty can also produce mechanical correction of the spine. Based on the assumption that surgery on the rib cage can alter the equilibrium of forces acting on the spine, this study aims to investigate the biomechanical role of the ribs during the surgical treatment of scoliosis using a finite element model of the spine and rib cage. The model was generated from patient-specific geometric data. Concave side rib shortening and convex side rib lengthening have been simulated and evaluated. Slight post-operative immediate geometrical correction of the spine was found in any of the simulations. However, both kinds of simulation induced similar loads on the vertebral endplates. Resulting torques in the frontal plane tended to correct the scoliotic spine in the frontal plane acting against vertebral wedging. Important torques were also found in the sagittal plane, increasing the physiological kyphosis, and derotational torques promoted the improvement of the transverse plane deformation. This biomechanical analysis showed that appropriate rib surgery may counteract the progression of the spine deformity depending on the remaining growth potential. These findings support the concept of early interventions on the rib cage that may be a new approach of treatment to prevent curve progression in small to moderate idiopathic scoliotic deformities.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".