The Prevalence, Incidence, and Progression of Hand Osteoarthritis in Relation to Body Mass Index, Smoking, and Alcohol Consumption
Bibliographic record
Abstract
OBJECTIVE: To estimate the extent that overweight/obesity, smoking, and alcohol are associated with prevalence and longitudinal changes of radiographic hand osteoarthritis (OA). METHODS: Participants from the Osteoarthritis Initiative (n = 1232) were included, of whom 994 had 4-year followup data. In analyses on incident hand OA, only persons without hand OA at baseline were included (n = 406). Our exposure variables were overweight/obesity [body mass index (BMI), waist circumference], smoking (current/former, smoking pack-yrs), and alcohol consumption (drinks/week). Using linear and logistic regression analyses, we analyzed possible associations between baseline exposure variables and radiographic hand OA severity, erosive hand OA, incidence of hand OA, and radiographic changes. Analyses were adjusted for age, sex, and education. RESULTS: Neither overweight nor obesity were associated with hand OA. Current smoking was associated with less hand OA in cross-sectional analyses, whereas longitudinal analyses suggested higher odds of incident hand OA in current smokers (OR 2.20, 95% CI 1.02-4.77). Moderate alcohol consumption was associated with higher Kellgren-Lawrence sum score at baseline (1-3 drinks: 1.55, 95% CI 0.43-2.67) and increasing sum score during 4-year followup (4-7 drinks: 0.33, 95% CI 0.01-0.64). Moderate alcohol consumption (1-7 drinks/week) was associated with 2-fold higher odds of erosive hand OA, which was statistically significant. Additional adjustment for BMI gave similar strengths of associations. CONCLUSION: Overweight/obesity were not associated with hand OA. Contrasting results were observed for smoking and hand OA, suggesting lack of association. Moderate alcohol consumption was associated with hand OA severity, radiographic changes, and erosive hand OA, warranting further investigation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".