Negative interaction between smoking and EBV in the risk of multiple sclerosis: The EnvIMS study
Bibliographic record
Abstract
BACKGROUND: Results from previous studies on a possible interaction between smoking and Epstein-Barr virus (EBV) in the risk of multiple sclerosis (MS) are conflicting. OBJECTIVES: To examine the interaction between smoking and infectious mononucleosis (IM) in the risk of MS. METHODS: Within the case-control study on Environmental Factors In Multiple Sclerosis (EnvIMS), 1904 MS patients and 3694 population-based frequency-matched healthy controls from Norway, Italy, and Sweden reported on prior exposure to smoking and history of IM. We examined the interaction between the two exposures on the additive and multiplicative scale. RESULTS: Smoking and IM were each found to be associated with an increased MS risk in all three countries, and there was a negative multiplicative interaction between the two exposures in each country separately as well as in the pooled analysis ( p = 0.001). Among those who reported IM, there was no increased risk associated with smoking (odds ratio (OR): 0.95, 95% confidence interval (CI): 0.66-1.37). The direction of the estimated interactions on the additive scale was consistent with a negative interaction in all three countries (relative excess risk due to interaction (RERI): -0.98, 95% CI: -2.05-0.15, p = 0.09). CONCLUSION: Our findings indicate competing antagonism, where the two exposures compete to affect the outcome.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".