The risk of systemic lupus erythematosus associated with Epstein–Barr virus infection: a systematic review and meta-analysis
Bibliographic record
Abstract
Previous systematic reviews have found a higher sero-prevalence of EBV antibodies in SLE patients compared with controls. Because many studies have been published, there is a need to apply more precise systematic review methods. We examined the association between EBV and SLE patients by conducting a systematic review and meta-analysis of case-control studies that examined the prevalence of EBV antibodies and the DNA-positive rate. We searched the MEDLINE and EMBASE databases from 1966 to 2018 with no language restrictions. The Mantel-Haenszel odds ratios (OR) for EBV antibody sero-positivity were calculated, and meta-analyses were conducted. Quality assessment was performed using a modified version of the Newcastle-Ottawa scale, and 33 studies were included. Most studies found a higher sero-prevalence of VCA IgG and EA IgG in SLE patients compared with controls. Meta-analysis demonstrated a significantly higher OR for sero-positivity to VCA IgG and EA IgG for SLE cases (2.06 [95% confidence interval (CI) 1.30-3.26, p = 0.002] and 7.70, [95% CI 4.64-12.76, p < 0.001], respectively). The overall OR for the DNA-positive rate for SLE patients compared with controls was 3.86 (95% CI 1.52-9.83, p = 0.005). Other antibodies, i.e., VCA IgA/IgM, EBNA IgA, and EA IgA/IgM, also demonstrated a significant difference between SLE patients and controls. These findings support previous systematic reviews; however, publication bias cannot be excluded. The methodological conduct of studies could be improved, particularly when selecting controls and analyses of laboratory conduct.
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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.033 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".