Cigarette Smoking, Alcohol Consumption, and Risk of Systemic Lupus Erythematosus: A Case-control Study in a Japanese Population
Bibliographic record
Abstract
OBJECTIVE: Cigarette smoking may be associated with increased risk of systemic lupus erythematosus (SLE), whereas the role of alcohol consumption is unknown. We examined the association between SLE risk and smoking or drinking. METHODS: We investigated the relationship of smoking and drinking compared to SLE risk among 171 SLE cases and 492 healthy controls in female Japanese subjects. Unconditional logistic regression was used to compute OR and 95% CI, with adjustments for several covariates. RESULTS: Compared with nonsmoking, current smoking was significantly associated with increased risk of SLE (OR 3.06, 95% CI 1.86-5.03). The higher the level of exposure to cigarette smoke, the higher the risk of SLE. Inhalation was also associated with increased SLE risk (OR 3.73, 95% CI 1.46-9.94 for moderate inhalation; OR 3.06, 95% CI 1.81-5.15 for deep inhalation). In contrast, light/moderate alcohol consumption had a protective effect on SLE risk (OR 0.38, 95% CI 0.19-0.76). As for beer, the risks for non-beer drinkers and beer drinkers were similar. This also applies to alcoholic beverages other than beer. CONCLUSION: Our results suggest that smoking was positively associated with increased SLE risk whereas light/moderate alcohol consumption was inversely associated with SLE risk, irrespective of the type of alcoholic beverage. Additional studies are warranted to confirm these findings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".