Delayed Gadolinium-Enhanced Magnetic Resonance Imaging of Cartilage: Clinical Associations in Obese Adults
Bibliographic record
Abstract
OBJECTIVE: Delayed gadolinium-enhanced magnetic resonance imaging (MRI) of cartilage (dGEMRIC) is used to assess cartilage glycosaminoglycan distribution. Our aim was to determine the relationships between self-reported pain and disability, clinical variables, and serum leptin, and dGEMRIC indices in obese subjects with and without clinical knee osteoarthritis (OA). METHODS: Seventy-seven subjects were recruited from laparoscopic adjustable gastric banding or exercise and diet-weight loss programs. The dGEMRIC index was assessed on MRI according to established protocol. Regression analysis adjusted for age, sex, body mass index (BMI), and presence of clinical knee OA. RESULTS: Mean age and BMI were 51 +/- 12.7 years and 39.6 +/- 6.2 kg/m(2). Twenty-three subjects (30%) had clinical knee OA (American College of Rheumatology criteria). The medial and lateral dGEMRIC indices were 538 +/- 80 ms and 539 +/- 86 ms. Age correlated negatively with medial (r = -0.40, p < 0.001) and lateral (r = -0.29, p = 0.012) dGEMRIC index. Subjects with clinical knee OA had significantly lower medial dGEMRIC index; however, no association was found for BMI. Varus alignment correlated with lower medial dGEMRIC index (r = -0.43, p < 0.006), while quadriceps strength correlated positively with lateral dGEMRIC index (r = 0.32, p = 0.008). There was also a negative correlation between serum leptin and lateral dGEMRIC index in women (r = -0.39, p = 0.035), with a trend in men (r = -0.52, p = 0.08). There were weak associations with physical disability, as self-reported on the WOMAC questionnaire. CONCLUSION: In obese subjects, knee dGEMRIC index was associated with age, clinical knee OA, abnormal tibiofemoral alignment, and quadriceps strength. Longitudinal studies are required to assess the potential for improvement in dGEMRIC index with interventions such as strength training.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".