Prediction of spinal deformity in scoliosis from geometric torsion
Bibliographic record
Abstract
The shape of a curved line that passes through thoracic and lumbar vertebrae is often used to study spinal deformity with measurements in "auxiliary" planes that are not truly three-dimensional (3D). Here we propose a new index, the geometric torsion, which could uniquely describe the spinal deformity. In this study we assessed whether geometric torsion could be effectively used. to predict spinal deformity with the aid of multiple linear regression. Anatomical landmarks were obtained from multi-view radiographic reconstruction and used to generate 3D model of the spine and rib cage of 28 patients. Fourier series best fitted to the vertebral centroids approximated the spinal shape. For each patient, spinal deformity indices were computed. Torsion was calculated and 20 derived parameters were recorded. Torsion inputs were used in a multiple linear regression model for prediction of key spinal indices. The primary clinical Cobb angle (mainly thoracic) was predicted well, with r=0.89 using all 20 inputs of torsion or r=0.83 using just two. Torsion was also well related to the orientation of plane of maximal deformity (r=0.87). Torsion was less accurate but still significant in predicting maximal vertebral axial rotation (r=0.77). This preliminary study showed promising results for the use of geometric torsion as an alternative 3D index of spinal deformity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".