Epstein–Barr virus infection after adolescence and human herpesvirus 6A as risk factors for multiple sclerosis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Infections with human herpesvirus 6A (HHV-6A) and Epstein-Barr virus (EBV) have been linked to multiple sclerosis (MS) development. For EBV, late infection has been proposed as a risk factor, but serological support is lacking. The objective of this study was to investigate how age affects the EBV and HHV-6A associated risks of developing MS. METHODS: In this nested case-control study, Swedish biobanks were accessed to find pre-symptomatically collected blood samples from 670 individuals who later developed relapsing MS and 670 matched controls. A bead-based multiplex assay was used to determine serological response against EBV and HHV-6A. Conditional logistic regression was used to calculate odds ratios and 95% confidence intervals. RESULTS: Seropositivity against EBV exhibited a pattern where associations switched from a decreased risk of developing MS in the group below 20 years of age to an increased risk amongst individuals aged 20-29 and 30-39 years (p for trend 0.020). The age of transition was estimated to be 18.8 years. In contrast, HHV-6A was associated with increased MS risk in all age groups (total cohort odds ratio 2.1, 95% confidence interval 1.6-2.7). CONCLUSIONS: This study suggests EBV infection after adolescence and age independent HHV-6A infection as risk factors for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".