Association Between Cigarette Smoking and Systemic Lupus Erythematosus: An Updated Multivariate Bayesian Metaanalysis
Bibliographic record
Abstract
OBJECTIVE: The association between cigarette smoking and the risk of systemic lupus erythematosus (SLE) remains a matter for debate. Additionally, the effect of the change of smokers' demographics on the risk of development of SLE over time has not been formally addressed. We aimed to examine the association between cigarette smoking and the risk of SLE by performing an updated metaanalysis. METHODS: A literature search using keywords including "lupus," "smoking," "cigarette," "environmental," "autoimmune," and "connective tissue disease" was performed in computerized databases to identify studies addressing the relationship between cigarette smoking and SLE occurrence. A Bayesian metaanalysis was conducted by computing the log-OR between current and never smokers, and between former and never smokers. The average log-OR (subsequently converted to OR) and their corresponding 95% credible intervals (CrI) were calculated. The effect of publication time, sex, and age of patients with SLE on the effect sizes was examined by multivariate metaregression. RESULTS: Data aggregation of 12 eligible studies comprising 3234 individuals who developed SLE and 288,336 control subjects revealed a significant association between SLE occurrence and current smoking status (OR 1.54, 95% CrI 1.06-2.25), while only a non-significant trend was demonstrated between SLE occurrence and former smoking status (OR 1.39, 95% CrI 0.95-2.08). Publication time, sex, and the mean age of patients with SLE did not explain the heterogeneity of the effect sizes. CONCLUSION: Current smoking status is associated with risk of SLE. Sex and age of patients with SLE had no significant effect on the risk of SLE over time.
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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 | 0.042 | 0.070 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".