Variations in Hip Shape Are Associated with Radiographic Knee Osteoarthritis: Cross-sectional and Longitudinal Analyses of the Johnston County Osteoarthritis Project
Bibliographic record
Abstract
OBJECTIVE: Hip shape by statistical shape modeling (SSM) is associated with hip radiographic osteoarthritis (rOA). We examined associations between hip shape and knee rOA given the biomechanical interrelationships between these joints. METHODS: Bilateral baseline hip shape assessments [for those with at least 1 hip with a Kellgren-Lawrence arthritis grading scale (KL) 0 or 1] from the Johnston County Osteoarthritis Project were available. Proximal femur shape was defined on baseline pelvis radiographs and evaluated by SSM, producing mean shape and continuous variables representing independent modes of variation (14 modes = 95% of shape variance). Outcomes included prevalent [baseline KL ≥ 2 or total knee replacement (TKR)], incident (baseline KL 0/1 with followup ≥ 2), and progressive knee rOA (KL increase of ≥ 1 or TKR). Limb-based logistic regression models for ipsilateral and contralateral comparisons were adjusted for age, sex, race, body mass index (BMI), and hip rOA, accounting for intraperson correlations. RESULTS: We evaluated 681 hips and 682 knees from 342 individuals (61% women, 83% white, mean age 62 yrs, BMI 29 kg/m(2)). Ninety-nine knees (15%) had prevalent rOA (4 knees with TKR). Lower modes 2 and 3 scores were associated with ipsilateral prevalent knee rOA, and only lower mode 3 scores were associated with contralateral prevalent knee rOA. No statistically significant associations were seen for incident or progressive knee rOA. CONCLUSION: Variations in hip shape were associated with prevalent, but not incident or progressive, knee rOA in this cohort, and may reflect biomechanical differences between limbs, genetic influences, or common factors related to both hip shape and knee rOA.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".