Associations of Alcohol Use with Radiographic Disease Progression in African Americans with Recent-onset Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the associations of alcohol consumption and radiographic disease progression in African Americans with recently diagnosed rheumatoid arthritis (RA). METHODS: Patients with RA included in the study were participants in the Consortium for the Longitudinal Evaluation of African Americans with Early Rheumatoid Arthritis (CLEAR) registry. Patients were categorized based on self-reported alcohol consumption; those consuming < 15 beverages per month versus those with ≥ 15 per month. Association of radiographic disease progression over a 1-year to 3-year period of observation with alcohol consumption was evaluated using multivariate generalized estimating equations. RESULTS: Of 166 patients included in the study, 39% reported that they had never consumed alcohol. Of the 61% who had consumed alcohol, 73% reported that they consumed on average < 15 alcoholic beverages per month and 27% reported consuming ≥ 15 per month. In multivariate analysis, consumption of ≥ 15 alcoholic beverages per month was associated with an increased risk of radiographic disease progression (p = 0.017). There was no evidence of a relationship in those consuming < 15 beverages per month (p = 0.802). CONCLUSION: There appears to be a dose-dependent relationship between alcohol use and radiographic disease progression in RA. Individuals who consume 15 or more alcoholic beverages per month may have faster rates of radiographic joint damage than those with lower levels of consumption.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".