Smokers with Multiple Sclerosis Are More Likely to Report Comorbid Autoimmune Diseases
Bibliographic record
Abstract
BACKGROUND/AIMS: Smoking is a risk factor for multiple sclerosis (MS) and autoimmune disease, and might explain an increased risk of comorbid autoimmune disease (CAD) in MS. We compared the risk of CAD in smokers and nonsmokers with MS. METHODS: Participants enrolled in the North American Research Committee on Multiple Sclerosis Registry reported their smoking status, the presence of CAD and the year of diagnosis. We used multivariable logistic regression to determine the independent association between smoking and CAD. We also compared the risk of developing a CAD in current smokers versus never-smokers who did not report any CAD at MS onset, using a proportional hazards model. RESULTS: Among 8,875 participants reporting comorbidities and smoking status, 1,649 (18.5%) reported a CAD. In a multivariable logistic model, ever-smokers had increased odds of reporting a CAD (odds ratio: 1.22; 95% CI: 1.08-1.38). Among the 7,830 participants without a CAD at onset of MS who reported their smoking status, including the age at which they started smoking, 3,035 (36.8%) currently smoked, while 3,805 (48.6%) never smoked. After adjustment, smokers had an increased risk of developing any autoimmune disease (hazard ratio: 1.23; 95% CI: 1.08-1.41) after MS onset. CONCLUSION: Smoking is associated with an increased risk of CAD in MS.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".