Excess body weight and four‐year function outcomes: Comparison of African Americans and whites in a prospective study of osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: We evaluated whether African Americans in the Osteoarthritis Initiative (OAI) have a greater risk (versus whites) of poor 4-year function outcome within strata defined by sex, body mass index (BMI), and waist circumference. METHODS: Using Western Ontario and McMaster Universities Osteoarthritis Index function, 20-meter walk, and chair stand performance, poor outcome was defined as moving into a worse function group or remaining in the 2 worst groups over 4 years. Logistic regression was used to evaluate the relationship between racial group and outcome within each stratum, adjusting for age, education, and income, and then further adjusting for BMI, comorbidity, depressive symptoms, physical activity, knee pain, and osteoarthritis (OA) severity. RESULTS: In 3,695 persons with or at higher risk for knee OA, higher BMI and large waist circumference were each associated with poor outcome. Among women with high BMI and among women with large waist circumference, African Americans were at greater risk for poor outcome by every measure, adjusting for age, education, and income. From fully adjusted models, potential explanatory factors included income, comorbidity, depressive symptoms, pain, and disease severity. Findings were less consistent for men, emerging only for the 20-meter walk or chair stand outcomes, and potentially explained by age and knee pain. CONCLUSION: Among OAI women with excess body weight, African Americans are at greater risk than whites for poor 4-year outcome. Modifiable factors that may help to explain these findings in the OAI include comorbidity, depressive symptoms, and knee pain. Targeting such factors, while supporting weight loss, may help to lessen the outcome disparity between African American and white women.
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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 | 0.001 |
| 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".