Is Hodgkin Lymphoma Associated with Hepatitis B and C Viruses? A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Apart from the Epstein-Barr virus (EBV), the etiology of the hematologic malignancy Hodgkin lymphoma (HL) is not well defined. Hepatitis B virus (HBV) and hepatitis C virus (HCV) are associated with some lymphoproliferative diseases with similarities to HL. METHODS: We performed a systematic review and meta-analysis, by searching Embase, MEDLINE, and Web of Science databases on March 9, 2021, for studies reporting a measure of association for HBV and HL or HCV and HL. We calculated pooled relative risks (RR) and their 95% confidence intervals (CI). RESULTS: Pooling nine HBV studies with 1,762 HL cases yielded an RR of 1.39 (95% CI, 1.00-1.94) and pooling 15 HCV studies with 4,837 HL cases resulted in an RR of 1.09 (95% CI, 0.88-1.35). Meta-analyzing by study design, hepatitis detection method, and region revealed two subgroups with statistically significant associations-HCV studies that used hospital-based controls and/or were conducted in the West Pacific. No included study assessed age or EBV tumor status in relation to HL. CONCLUSIONS: Although we did not find an association between HBV or HCV and HL, research assessing the impact of age and EBV tumor status was lacking. IMPACT: The effect of HBV or HCV infection in the development of HL remains unclear.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | low |
| gpt | no category Domain: not available · Genre: Review About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".