Occurrence of Radiographic Osteoarthritis of the Knee and Hip Among African Americans and Whites: A Population‐Based Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To compare the incidence and progression of radiographic osteoarthritis (OA) in the knee and hip among African Americans and whites. METHODS: Using the joint as the unit of analysis, we analyzed data from the Johnston County Osteoarthritis Project, a population-based prospective cohort study in rural North Carolina. Baseline and followup assessments were 3-13 years apart. Assessments included standard knee and hip radiographs read for Kellgren/Lawrence (K/L) radiographic grade. Weighted analyses controlled for age, sex, body mass index, level of education, and baseline K/L grade; bootstrap methods adjusted for lack of independence between left and right joints. Time-to-event analysis was used to analyze the data. RESULTS: For radiographic knee OA, being African American had no association with incidence (adjusted hazard ratio [HRadj ] 0.80, 95% confidence interval [95% CI] 0.53-1.22), but had a positive association with progression (HRadj 1.67, 95% CI 1.05-2.67). For radiographic hip OA, African Americans had a significantly lower incidence (HRadj 0.44, 95% CI 0.27-0.71), whereas the association with progression was positive but nonsignificant (HRadj 1.46, 95% CI 0.53-4.01). In sensitivity analyses, the association with hip OA incidence was robust to a wide range of assumptions. CONCLUSION: African Americans are protected against incident hip OA, but may be more susceptible to progressive knee 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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".