Bibliographic record
Abstract
Epstein-Barr virus (EBV), low levels of vitamin-D, smoking and the haplotype containing HLA-DRB1 have consistently been shown to be associated with an increased risk of MS. However, only modest associations have been found. An attractive hypothesis is that interactions between these factors may help to explain the variation in risk. We studied evidence of interaction between smoking and a history of infectious mononucleosis (IM, as a proxy for EBV) in a large multinational case-control study including subjects from Italy, Norway, Sweden, Serbia and Canada (EnvIMS). Analyzing currently included 2125 cases and 4455 controls we found that both IM and smoking, individually, were important factors in all countries included. Adjusted for age and sex the pooled odds ratio (OR) was 2.0 (95% CI: 1.6-2.4) for IM and of 1.8 (95% CI: 1.6-2.4) for smoking. We found that the effect of IM was significantly higher among non-smokers, pooled OR of 2.4 (95% CI: 1.8-3.2), than among smokers, pooled OR of 1.6 (95% CI: 1.3-2.1), p-value = 0.04 using multiplicative interaction term in logistic regression. This negative interaction was found in all countries included and among men and women separately. While the case-control design has limitations related to the retrospective ascertainment of risk factors, it is unlikely that the negative interaction in this study could be the result of recall bias. This observation does not support the hypothesis that the risk of MS related to EBV is increased by the presence of smoking. Indeed, our results are consistent with the suggestion that EBV (as measured by IM) and smoking represent different pathogenic pathways in MS.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.785 | 0.541 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".