Computerized Assessment of Sagittal Curvatures of the Spine
Bibliographic record
Abstract
OBJECTIVE: The tangent circles technique has been proposed as an alternative to the Cobb angle technique to assess sagittal curves of the spine. However, it has never been compared directly to the Cobb technique. This study compares the reproducibility and clinical relevance of the maximum Cobb angle and tangent circles techniques. METHOD: Standing sagittal radiographs of the spine of 10 adolescents with idiopathic scoliosis, 10 adolescents with spondylolisthesis, and 10 healthy adolescents were used. Thoracic kyphosis (TK) and lumbar lordosis (LL) were measured by 3 observers using the maximum Cobb angle and the tangent circles techniques. Intra- and interobserver intraclass correlation coefficients (ICCs) were calculated. RESULT: Intra- and interobserver ICCs for TK were 0.88 and 0.85, respectively, for the maximum Cobb angle technique, and 0.94 and 0.83, respectively, for the tangent circles technique. Intra- and interobserver ICCs for LL were 0.97 and 0.77, respectively, for the maximum Cobb angle technique, and 0.88 and 0.94, respectively, for the tangent circles technique. The 2 techniques were highly correlated for the measurement of the TK (r=0.93) and LL (r=0.88). CONCLUSION: Both techniques provide excellent intra- and interobserver reproducibility. Tangent circles technique may be a good alternative to the Cobb angle technique because it allows the evaluation of the global geometry of sagittal spinal curves, especially when there is limited visibility of bony structures on 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".