VEPTR IMPLANTATION TO TREAT CHILDREN WITH EARLY-ONSET SCOLIOSIS WITHOUT RIB ABNORMALITIES: A PROSPECTIVE MULTICENTRE STUDY
Bibliographic record
Abstract
To evaluate the efficacy of VEPTR in preventing further progression of scoliosis without impeding spinal growth in the treatment of children with progressive early onset scoliosis (EOS) without rib abnormalities. Prospective, multi-center, observational cohort study on patients with EOS treated with VEPTR with 2-year follow up. Data were analysed based on measurements done pre-implant, immediate post-op and at 2-yr f/u. Sixty-three patients met inclusion: 35 males and 28 females. Mean age at time of implantation was 6.1±2.4 yrs. Etiologies included congenital (n=6), neuromuscular (n=36), syndromic (n=4), and idiopathic (n=17). Mean follow up was 2.2±0.4 yrs. Scoliosis (72o±18o) decreased after implant surgery (47o±17o) followed by slight increase at 2-yr f/u (57o±18o), p At 2-yr f/u, VEPTR was effective in treating EOS without rib abnormalities with 86% of patients having an improvement in scoliosis and 94% of patients having an increased spinal height as compared to pre-operatively. VEPTR provided greater than 100% of expected age-matched spine growth and the instrumented spinal segment continued to grow during distraction phase. This large prospective, multicentre study demonstrated the ability of VEPTER to effectively treat EOS without rib abnormalities. Goals of preventing further scoliosis progression and of maintaining normal spine growth were achieved.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".