Exploring the effect of alcohol on disease activity and outcomes in rheumatoid arthritis through systematic review and meta-analysis
Bibliographic record
Abstract
Abstract To evaluate the effects of alcohol consumption on disease activity in rheumatoid arthritis. EMBASE, Pubmed, the Cochrane Library, and Web of Science were searched until July 29, 2020. English language studies that reported disease activity outcomes in rheumatoid arthritis were included. Studies were excluded if they were reviews, case reports, had fewer than 20 patients, or reported on prevalence but not disease activity in RA. Forest plots were used to determine pooled mean difference and were generated on RevMan5.3. Linear regression was used to determine correlations between alcohol and antibody status, gender, and smoking status. The search identified 4126 citations of which 14 were included. The pooled mean difference in DAS28 (95% CI) was 0.34 (0.24, 0.44) ( p < 10 −5 ) between drinkers and non-drinkers with lower DAS28 in non-drinkers, 0.33 (0.05, 0.62) ( p = 0.02) between heavy drinkers and non-drinkers with lower DAS28 in heavy drinkers, and 0.00 (− 0.30, 0.30) ( p = 0.98) between low- and high-risk drinkers. The mean difference of HAQ assessments was significantly different between those who drink alcohol compared to those who do not, with drinkers reporting lower HAQ scores (0.3 (0.18, 0.41), p < 10 −5 ). There was no significant correlation between drinking and gender, smoking status, or antibody positivity. Alcohol consumption is associated with lower disease activity and self-reported health assessment in rheumatoid arthritis. However, drinking has no correlation with smoking, gender, or antibody status.
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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 | 0.020 | 0.047 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.039 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".