Association of Infectious Mononucleosis with Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Genetic and environmental factors have important roles in multiple sclerosis (MS) susceptibility. Several studies have attempted to correlate exposure to viral illness with the subsequent development of MS. Here in a population-based Canadian cohort, we investigate the relationship between prior clinical infection or vaccination and the risk of MS. METHODS: Using the longitudinal Canadian database, 14,362 MS index cases and 7,671 spouse controls were asked about history of measles, mumps, rubella, varicella and infectious mononucleosis as well as details about vaccination with measles, mumps, rubella, hepatitis B and influenza vaccines. Comparisons were made between cases and spouse controls. RESULTS: Spouse controls and stratification by sex appear to correct for ascertainment bias because with a single exception we found no significant differences between cases and controls for all viral exposures and vaccinations. However, 699 cases and 165 controls reported a history of infectious mononucleosis (p < 0.001, corrected odds ratio 2.06, 95% confidence interval 1.71-2.48). Females were more aware of disease history than males (p < 0.001). CONCLUSIONS: The data further confirms a reporting distortion between males and females. Historically reported measles, mumps, rubella, varicella and vaccination for hepatitis B, influenza, measles, mumps and rubella are not associated with increased risk of MS later in life. A clinical history of infectious mononucleosis is conspicuously associated with increased MS susceptibility. These findings support studies implicating Epstein-Barr virus in MS disease susceptibility, but a co-association between MS susceptibility and clinically apparent infectious mononucleosis cannot be excluded.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".