Three-dimensional Subclassification of Lenke Type 1 Scoliotic Curves
Bibliographic record
Abstract
STUDY DESIGN: Prospective study of the 3-D shape variability of spinal curve in Lenke type 1 adolescent idiopathic scoliosis (AIS). OBJECTIVES: To determine the statistical 3-D variability of Lenke type 1 curves and to evaluate clinical parameters that can be integrated to refine the Lenke et al original proposal, and to pave the road for a comprehensive 3-D subclassification of AIS. SUMMARY OF BACKGROUND DATA: Several classification systems based on the identification of key features from frontal and sagittal x-rays have been proposed in AIS, but these remain an oversimplification of the complex 3-D deformity because it is only based on 2-D imaging. Clinical 3-D parameter variability has been investigated in previous studies, but has never been considered in the context of the Lenke classification. METHODS: Radiographs of 68 AIS patients with Lenke type 1 curves were reconstructed in 3-dimension using a stereo-radiographic technique and were submitted to a computer algorithm to compute a set of 3-D parameters that can be used to characterize the 3-D curve. Cluster analysis was performed to determine the statistical distribution of 3-D parameters among Lenke 1 curve types. RESULTS: Statistical analysis shows specific 3-D deformation patterns within Lenke type 1 curves, mostly using the best-fit plane or BFP (SD+/-22.9, +/-49.8) and geometric torsion parameters. No significant variability was found using the plane of maximum curvature or PMC. CONCLUSIONS: Recent advances in computer vision facilitate the introduction of 3-D reconstruction in a standard clinical setting and can provide more information toward the spine behavior in 3-D space. A direct consequence of commonly used 3-D reconstruction would be to be able to evaluate 3-D indices and to devise a real 3-D classification system from the Lenke et al proposal.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".