Aortic, musculoskeletal and organ characteristics on computed tomography in knee osteoarthritis – an explorative study in the IMI-APPROACH cohort
Bibliographic record
Abstract
Abstract The systemic associations with knee osteoarthritis (KOA) are incompletely understood. This study explores aortic disease, musculoskeletal and organ findings in patients with KOA in relation to their symptoms or radiographic abnormalities. Full body computed tomography (CT) scans of 255 IMI-APPROACH participants were investigated using an automated analysis of multislice CT (Voronoi Health Analytics) that extracts aortic size and calcifications, and volumes and densities of bones, muscles, fat compartments and thoracic and abdominal organs. The CT measurements were primarily related to KOA as measured with Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), visual scores and automated knee radiograph analysis of osteophytes, bone sclerosis and joint space width. The median age was 67 years, body mass index (BMI) 26.8 kg/m 2 and 78% were female. About half had Kellgren-Lawrence grade ≥ 2. Larger knee osteophyte area was associated with a larger aortic volume (R Spearman =0.21, P = 0.001), which can be due to elongation or dilatation. We observed an association between more symptoms and increased psoas (R Spearman =-0.23, P < 0.001) and lower leg (R Spearman =-0.23, P < 0.001) muscle density, suggesting less microscopic muscle fat. Symptomatic KOA was associated with substantially lower lung volume (771 ml difference between 50% worst and 50% best WOMAC), but not with visible lung disease. Lung volume and density were significantly associated with the physical functioning WOMAC component. These associations remained significant after adjustment for age, sex and BMI. KOA is associated with significant systemic changes, including altered aortic and organ volumes. These correlations suggest that KOA’s impact may extend beyond the joints. Future research should explore the causal relationships and therapeutic implications associations.
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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) | 0.000 | 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 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".