Quantifying the Association of Radiographic Osteoarthritis in Knee or Hip Joints with Other Knees or Hips: The Johnston County Osteoarthritis Project
Bibliographic record
Abstract
OBJECTIVE: To quantify the association of radiographic osteoarthritis (ROA) in one knee or hip joint with other knee or hip joints. METHODS: We analyzed baseline data from the Johnston County Osteoarthritis Project (n = 3068). We fit 4 models for left/right knee/hip. The Kellgren-Lawrence (KL) radiographic grade severity scale was KL 0/1 (no/questionable ROA), 2 (mild ROA), or 3/4 (moderate/severe ROA). We estimated associations between KL grade in contralateral joints and other joint sites (e.g., worst hip in knee models), adjusting for sex, race/ethnicity (African American/white), age, and measured body mass index, using cumulative odds logistic regression models. Interactions were investigated: race/ethnicity by sex; race/ethnicity and sex by the 2 explanatory variables. RESULTS: Contralateral joint KL grade was strongly associated with KL grade, with OR ranging from 9.2 (95% CI 7.1, 11.9) to 225.0 (95% CI 83.6, 605.7). In the left knee model, the contralateral joint association was stronger among African Americans than whites, but for the other models the associations by race/ethnicity were identical. Models examining other joint sites showed weaker but mostly statistically significant associations (OR 1.4 to 1.8). CONCLUSION: We found a strong multivariable-adjusted association between KL grades in contralateral knees and hips, and a modest association with the other joint site (e.g., knees vs hips). These results suggest that diagnosis of ROA in 1 large joint may be a marker for risk of multijoint ROA, and warrant interventions to reduce the incidence or severity of ROA at these other joints.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".