Prediction of Apical Vertebral Rotation in Adolescent Idiopathic Scoliosis Using Bayesian Regression Analysis
Bibliographic record
Abstract
Study Design: Retrospective cross-sectional study.Objectives: This study aimed to predict apical vertebral rotation (AVR) based on curve morphology using Bayesian statistics in patients with adolescent idiopathic scoliosis (AIS).Summary of Literature Review: Although AVR can be measured on axial CT images, the ability to simply predict AVR based on radiographs via Bayesian analysis would aid surgeons in surgical planning while avoiding excessive radiation.Materials and Methods: In total, 1110 patients who underwent deformity correction due to AIS were retrospectively reviewed.Based on Cobb's angle and coronal alignment parameters, AVR was calculated using Bayesian statistical models.Results: The Bayesian models successfully predicted AVR based on coronal alignment parameters.Conclusions: Significant correlations were found between the coronal alignment parameters of AIS and AVR.Furthermore, this model could be used to predict AVR using this model, which would potentially aid surgeons in preoperative planning.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".