Systematic Review and Meta-Analysis of the Sero-Epidemiological Association between Epstein Barr Virus and Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: A role for Epstein Barr virus (EBV) in multiple sclerosis (MS) has been postulated. Previous systematic reviews found higher prevalences of anti-EBV antibodies in MS patients compared to controls, but many studies have since been published, and there is a need to apply more rigorous systematic review methods. METHODOLOGY/PRINCIPAL FINDINGS: We examined the link between EBV and MS by conducting a systematic review and meta-analysis of case-control and cohort studies that examined the prevalence of anti-EBV antibodies in the serum of cases and controls. We searched Medline and Embase databases from 1960 to 2012, with no language restriction. The Mantel-Haenszel odds ratios (OR) for anti-EBV antibodies sero-positivity were calculated, and meta-analysis conducted. Quality assessment was performed using a modified version of the Newcastle Ottawa scale. Thirty-nine studies were included. Quality assessment found most studies reported acceptable selection and comparability of cases and controls. However the majority had poor reporting of ascertainment of exposure. Most studies found a higher sero-prevalence of anti-EBNA IgG and anti-VCA IgG in cases compared to controls. The results for anti-EA IgG were mixed with only half the studies finding a higher sero-prevalence in cases. The meta-analysis showed a significant OR for sero-positivity to anti-EBNA IgG and anti-VCA IgG in MS cases (4.5 [95% confidence interval (CI) 3.3 to 6.6, p<0.00001] and 4.5 [95% CI 2.8 to 7.2, p<0.00001] respectively). However, funnel plot examination suggested publication bias for the reporting of the anti-EBNA IgG. No significant difference in the OR for sero-positivity to anti-EA IgG was found (1.4 [95% CI 0.9 to 2.1, p = 0.09]). CONCLUSION/SIGNIFICANCE: These findings support previous systematic reviews, however publication bias cannot be excluded. The methodological conduct of studies could be improved, particularly with regard to reporting and conduct of laboratory analyses.
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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 | 0.024 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.036 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".