Radiographic Analysis of Lumbar Lordosis
Bibliographic record
Abstract
STUDY DESIGN: Delayed, repeated measures, with three examiners each twice digitizing thirty lateral lumbar radiographs. OBJECTIVES: To determine the reliability and clinical utility of the centroid, Cobb, tangential radiologic assessment of lumbar lordosis (TRALL), and Harrison posterior tangent line-drawing methods for analysis of lumbar lordosis. BACKGROUND DATA: Cobb's method is commonly used for curvature analysis on lateral lumbar radiographs, whereas the centroid, TRALL, and Harrison posterior tangent methods are not widely used. METHODS: Thirty lateral lumbar radiographs were digitized twice by each of three examiners. To evaluate reliability of determining global and segmental alignment, all four vertebral body corners of T12-S1 and the superior margin of the femur head were digitized. Angles created were segmental and global centroid, (two-line) Cobb angles, and intersections of posterior tangents. A global TRALL angle was determined. Means, standard deviations, mean absolute differences, interclass and intraclass correlation coefficients (ICC), and confidence intervals were calculated. RESULTS: The interobserver and intraobserver reliabilities of measuring all segmental and global angles were in the high range (ICCs > 0.83). The mean absolute differences of observers' measurements were small (0.6 degrees -2.0 degrees ). Distal segmental (L4-S1) and global angles of lumbar curvature were dependent on the method of measurement. CONCLUSIONS: All four radiographic methods had high reliability and low mean absolute differences of observers' measurements. Because it lacks a segmental analysis, the TRALL method is not recommended. The centroid, Cobb, and Harrison posterior tangent methods provide global and segmental angles. However, the centroid segmental method requires three segments and is less useful for a stability analysis.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".