Quantitative Measures of Modic Changes in Lumbar Spine Magnetic Resonance Imaging
Bibliographic record
Abstract
STUDY DESIGN: A measurement reliability study. OBJECTIVE: To develop quantitative measures for Modic changes (MCs) on magnetic resonance (MR) images and evaluate measurement reliability. SUMMARY OF BACKGROUND DATA: MCs have been studied for more than 20 years but the clinical significance remains controversial. Little effort has been made to improve the measurement of MCs. METHODS: The study was approved by the responsible institutional review board. On the basis of Modic classification, a series of quantitative dimension and signal intensity measures were developed for assessing MCs. Midsagittal T1- and T2-weighted MR images from 83 lumbar spines were then qualitatively and quantitatively assessed by two observers independently. Kappa and intraclass correlation coefficient (ICC) were used to examine intra- and inter-rater reliability. Pearson correlation coefficient was used to assess the relationships between the quantitative measurements of MCs. Mean absolute deviation (MAD) and Bland-Altman plots also were used to evaluate measurement errors and limits of agreement for selected measures. RESULTS: For Modic classification, intrarater agreement was excellent (κ = 0.88) and inter-rater agreement was substantial (κ = 0.79). Intrarater agreement also was excellent when obtaining dimension measurements (ICC = 0.82-0.96) from T1- or T2-weighted images and inter-rater agreement was slightly greater using T1-weighted images (ICC = 0.73-0.88) than T2-weighted images (ICC = 0.66-0.82). Signal intensity measurements on T2-weighted images were found to have almost perfect intra- and inter-rater reliability (ICC = 0.92-0.99). The correlation analysis demonstrated that the quantitative measures represent different constructs. The MAD and Bland-Altman Plots further confirmed the high reliability of the area ratio, MCs mean signal intensity and MCs total signal intensity measurements. CONCLUSION: Three quantitative measures are suggested to assess the severity of MCs, which provide reliable, precise measurements for research on the etiology, pathogenesis, and clinical relevance of MCs.
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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) | 0.000 | 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) | 0.001 | 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 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".