Smoking and secondhand smoke among patients with systemic lupus erythematosus and controls: associations with disease and disease damage
Bibliographic record
Abstract
Previous reports suggest smoking may be a risk factor for developing systemic lupus erythematosus (SLE). This study explores the impact of tobacco smoke on SLE patients compared with controls and on disease characteristics among patients. Data from a cohort of SLE patients and controls were utilized. Medical history, smoking and secondhand smoke exposure history, SLE Disease Activity Index (SLEDAI) and SLICC Damage Index (SDI) scores were collected at an in-person enrollment visit and confirmed by chart review. Statistical analysis used chi-square testing and multivariate logistic regression. There were 545 SLE patients and 386 controls with data available for analysis (Table 1 ). At enrollment, the mean age was 37.6 ± 14.7 years for patients and 42.0 ± 15.4 years for controls. Differences between current and never smokers ( P = 0.51) and ever and never smokers ( P = 0.70) were not significantly different between patients and controls. Compared with unrelated controls, African American patients were significantly more likely to be exposed in the home to secondhand smoke before the age of 18 (OR 1.81, 95% CI 1.13 to 2.89). Damage by SDI (SDI > 0) was significantly associated with ever smoking (OR 3.08, 95% CI 1.4 to 6.6), current smoking (OR 3.17, 95% CI 1.1 to 9.1), and secondhand smoke exposure in childhood (OR 1.91, 95% CI 1.0 to 3.6). No significant relationship was found between smoking status and active disease at enrollment (SLEDAI ≥ 6) or dsDNA autoantibodies. Discoid rash was significantly associated with ever smoking (OR 2.74, 95% CI 1.5 to 5.1) and current smoking (OR 4.85, 95% CI 2.2 to 10.5). Our study suggests that secondhand smoke during childhood may be a risk factor for SLE. Secondhand smoke during childhood, current smoking and past smoking contribute significantly to disease damage among patients with SLE.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".