Classification of Congenital Scoliosis and Kyphosis
Bibliographic record
Abstract
STUDY DESIGN: We reviewed three-dimensional (3D) computed tomography (CT) images of congenital spinal deformities and proposed a new classification based on the information obtained. OBJECTIVES: The purposes of this article were to clearly illustrate the limitations of two-dimensional classification, to summarize the clinical significance of 3D analysis of congenital vertebral anomalies, and to propose a new 3D classification of congenital vertebral anomalies. SUMMARY OF BACKGROUND DATA: The classification of congenital scoliosis or kyphosis were based on radiographic findings of plain radiograph images of congenital vertebral anomalies, it is sometimes difficult in classifying the large variety of anomalous vertebrae or severely twisted 3D curves. METHODS: Three-dimensional CT images of more than 150 patients with congenital spinal deformities were analyzed and compared with plain radiograph images. By developing the algorithm for the evaluation of malformed vertebrae in terms of numbers of abnormal vertebrae, type of formation failure, and type of segmentation failure in separate steps, we attempted to revise the classification of congenital spinal deformities. RESULTS: The images of plain radiograph cannot demonstrate the spatial relationship of each structure of the vertebrae. Three-dimensional findings in congenital-deformed vertebrae included several types of laminae and clearer definitions of each type of anomalous vertebrae. By developing an algorithm for the evaluation of congenital spinal deformity, congenital spinal deformity could be mainly classified into 4 types of congenital vertebral abnormalities: Type 1: solitary simple, Type 2: multiple simple, Type 3: complex, Type 4: segmentation failure. CONCLUSION: The large volume of information that can be obtained by evaluating 3D CT images of congenitally deformed vertebrae can be a great help in developing a strategy for surgical treatment. We need to develop a new classification of congenital scoliosis based on the perspective of 3D imaging to understand the etiology and embryology, as well as to determine an operative strategy.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".