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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".