Hand Joint Space Narrowing and Osteophytes Are Associated with Magnetic Resonance Imaging-defined Knee Cartilage Thickness and Radiographic Knee Osteoarthritis: Data from the Osteoarthritis Initiative
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether features of radiographic hand osteoarthritis (OA) are associated with quantitative magnetic resonance imaging (MRI)-defined knee cartilage thickness, radiographic knee OA, and 1-year structural progression. METHODS: A total of 765 participants in Osteoarthritis Initiative (OAI; 455 women, mean age 62.5 yrs, SD 9.4) obtained hand radiographs (at baseline), knee radiographs (baseline and Year 1), and knee MRI (baseline and Year 1). Hand radiographs were scored for presence of osteophytes and joint space narrowing (JSN). Knee radiographs were scored according to the Kellgren-Lawrence (KL) scale. Cartilage thickness in the medial and lateral femorotibial compartments was measured quantitatively from coronal FLASHwe images. We examined the cross-sectional and longitudinal associations between features of hand OA (total osteophyte and JSN scores) and knee cartilage thickness, 1-year knee cartilage thinning (above smallest detectable change), presence of knee OA (KL grade ≥ 3), and progression of knee OA (KL change ≥ 1) by linear and logistic regression. Both hand OA features were included in a multivariate model (if p ≤ 0.25) adjusted for age, sex, and body mass index (BMI). RESULTS: Hand JSN was associated with reduced knee cartilage thickness (ß = -0.02, 95% CI -0.03, -0.01) in the medial femorotibial compartment, while hand osteophytes were associated with the presence of radiographic knee OA (OR 1.10, 95% CI 1.03-1.18; multivariate models) with both hand OA features as independent variables adjusted for age, sex, and BMI). Radiographic features of hand OA were not associated with 1-year cartilage thinning or radiographic knee OA progression. CONCLUSION: Our results support a systemic OA susceptibility and possibly different mechanisms for osteophyte formation and cartilage thinning.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".