Reliability and validity of lumbar disc height quantification methods using magnetic resonance images
Bibliographic record
Abstract
Disc height has been a focus of research on disc degeneration and low back pain (LBP). However, choosing an appropriate method to quantify disc height remains controversial. The aim of the present study was to determine the reliability and construct validity of disc height quantification methods. Repeated semi-automatic measurements of L4-5 and L5-S1 discs were obtained from 43 T2-weighted mid-sagittal 3T magnetic resonance (MR) images of 22 subjects with LBP (43±13 years), blinded to prior measurements. Heights were calculated with area-based methods (using 60%, 80% and 100% of the disc width), and point-based methods (Hurxthal's, Dabbs' and combining the two). Intra-class correlation coefficients (ICC) and standard error of measurement (SEM) were estimated. Construct validity was assessed using correlation coefficients. Intra-rater ICC(3,1) of the area-based disc height measurements ranged from 0.84 to 0.99 with an inter-rater ICC(2,1) of 0.99. Measurements with point-based methods had lower intra- and inter-rater reliability ranging between 0.76 and 0.96 and between 0.84 and 0.98, respectively. Inter-rater SEM varied between 0.2 and 0.3 mm for area-based methods and between 0.3 and 0.7 mm for point-based methods. Excluding Dabbs', high correlations (r>0.9) were observed between methods. Area-based height measurements using partial disc width demonstrated excellent reliability and construct validity and outperformed point-based methods.
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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 | 0.021 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".