Ethnic differences in the relationship between obesity and joint pain and function in a joint arthroplasty population.
Bibliographic record
Abstract
OBJECTIVE: We investigated the influence of obesity on joint pain and function in Asians as compared to Caucasians with degenerative hip and knee arthritis. METHODS: We surveyed 1983 patients (1876 Caucasians and 107 Asians) undergoing primary hip or knee replacement surgery. Relevant covariates including demographic data, body mass index (BMI), sex, comorbidities, education, and ethnicity were recorded. Pain and joint functional status were assessed at baseline and at 1-year followup with the Western Ontario and McMaster University Osteoarthritis Index (WOMAC) pain and function scores. RESULTS: Asian patients presented for surgery at a significantly younger age and lower mean BMI, and reported greater pain and dysfunction than Caucasian patients. Multivariate linear regression modeling showed that for every level of BMI, Asian patients reported greater levels of joint pain and dysfunction. At a BMI of 30 kg/m2, this translated to a 16.6% higher WOMAC score (p < 0.001). CONCLUSION: Among patients with endstage osteoarthritis, at every level of BMI, joint pain and dysfunction are greater in Asians than in Caucasians. This difference is likely mediated through both mechanical and inflammatory effects.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".