Functional Classification of Patients With Idiopathic Scoliosis Assessed by the Quantec System
Bibliographic record
Abstract
STUDY DESIGN: A stepwise discriminant analysis was used to define a spinal deformity score based on three-dimensional measurements by the Quantec spinal image system (raster stereophotograph). OBJECTIVE: To provide functional classification of spinal deformity in patients with mild idiopathic scoliosis without using radiographs. SUMMARY OF BACKGROUND DATA: Most studies classify the degree of spinal deformity in terms of coronal plane radiograph without analyzing transverse rotation. To the authors' knowledge, no studies investigating classification of spinal deformity in idiopathic scoliosis using Quantec system measurements have been documented. METHODS: In this study, 129 patients with a single curve and 119 patients with a double curve were divided into three groups according to Cobb angle: Group 1 (less than 10 degrees ), Group 2 (10-20 degrees ), and Group 3 (greater than 20 degrees ). RESULTS: The patients were assigned to the group with the highest scores after application of a stepwise discriminant analysis. The accuracy of the classification system by functional scores for the patients with a single curve was 85% for Group 1, 63.5% for Group 2, and 71.7% for Group 3. The accuracy of classification by functional scores for the patients with a double curve was 87.1% for Group 2 and 76.1% for Group 3. CONCLUSION: The back surface image study is a method for providing a quantitative assessment of mild spinal deformity, allowing evaluation of patients by integrated three-dimensional parameters with no reference to radiographs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".