Features of Knee and Multijoint Osteoarthritis by Sex and Race and Ethnicity: A Preliminary Analysis in the Johnston County Health Study
Bibliographic record
Abstract
Objective To evaluate knee osteoarthritis (KOA) and multijoint osteoarthritis (MJOA), and to compare features by sex and race and ethnicity in a population-based cohort. Methods Participants (n = 544) enrolled in the Johnston County Health Study (JoCoHS) as of January 2023 were categorized by radiographic and symptomatic KOA and MJOA phenotypes, and frequencies were compared by sex and race and ethnicity. Symptoms were assessed according to the Knee Injury and Osteoarthritis Outcome Score (KOOS) and pain, aching, and stiffness (PAS) scores at various joints. Models produced estimates (odds ratio [OR] or mean ratios [MR] and 95% CI) adjusted for age, BMI (kg/m2), and education. Results Men had twice the odds of having MJOA-6 (≥ 3 lower extremity joints affected); there were no significant differences in MJOA phenotypes by race and ethnicity. Women had 50% higher odds of having KOA or having various features of KOA. Women reported significantly worse KOOS Symptoms scores (MR 1.25). Black participants had higher odds of more severe KOA (OR 1.47), subchondral sclerosis (OR 2.06), and medial tibial osteophytes (OR 1.50). Black participants reported worse KOOS Symptoms than White participants (MR 1.18). Although not statistically significant, Hispanic participants (vs non-Hispanic participants) appeared to have lower odds of radiographic changes but reported worse symptoms. Conclusion Preliminary findings in the diverse JoCoHS cohort suggest more lower extremity–predominant MJOA in men compared to women. Women and Black participants had more KOA features and more severe symptoms. Hispanic participants appear to have higher pain and symptoms scores despite having fewer structural changes. Studies in diverse populations are needed to understand the burden of OA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".