Delayed Gadolinium‐Enhanced Magnetic Resonance Imaging of Cartilage Values in Hips With Bone Marrow Lesions
Bibliographic record
Abstract
OBJECTIVE: Bone marrow lesions (BMLs) are associated with painful and progressive osteoarthritis (OA). Quantitative magnetic resonance imaging (MRI) has been used to study early cartilage degeneration in knees with BML, but similar work has not been done in hips. The purpose of this study was to compare mean delayed gadolinium-enhanced MRI of cartilage (dGEMRIC) relaxation values (T1Gd) in hips with BML to hips without BML in a population-based study. Reduced T1Gd suggests depleted glycosaminoglycan. Our hypothesis was that mean T1Gd is lower in hips with BML compared to hips without BML. METHODS: Study participants (n = 128) were recruited from a cross-sectional population-based study of people ages 20-49 years with and without hip pain. dGEMRIC and proton density (PD)-weighted MRI scans of 1 hip from each participant were used for this analysis. BMLs were identified from PD-weighted fat-suppressed images. We applied a sampling-weighted linear regression model to determine the association of the presence of BMLs with mean cartilage T1Gd (significance: P < 0.05). The model was adjusted for age, sex, body mass index (BMI), hip pain, cam/pincer deformity, and physical activity. RESULTS: Thirty-two (25%) of the 128 participants had at least 1 BML. Subjects with at least 1 BML, compared to those without, had similar weighted characteristics of age, BMI, physical activity levels, and frequency of hip pain. Mean T1Gd was 75.25 msec lower (95% confidence interval -149.69, -0.81; P = 0.048) (9%) in the BML compared to the no-BML group. CONCLUSION: Our results suggest that hips with BMLs are associated with hip cartilage degeneration early in the OA disease process.
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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 | 0.004 |
| 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".