Hepatitis B virus infection and the risk of gynecologic cancers: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVES: The relationship between hepatitis B virus (HBV) infection and gynecologic cancers is controversial. We aimed to evaluate the risk of gynecologic cancers associated with HBV infection using a meta-analysis. METHODS: Two independent reviewers identified publications in the PubMed, Embase and Cochrane Library databases that reported an association between HBV and the risk of gynecologic malignancy from inception to December 31, 2022. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the included articles. Pooled odds ratios (ORs) and 95% corresponding confidence intervals (CIs) were calculated using a fixed effects model or random effects model. RESULTS: We collected data from 7 studies that met the inclusion criteria, including 2 cohort studies and 5 case-control studies. HBV was significantly associated with the risk of cervical cancer in the general population (OR 1.22, 95% CI 1.09-1.38, P = 0.001), although the same trend was not found in endometrial cancer (OR 1.30, 95% CI 0.95-1.77, P = 0.105) and ovarian cancer (OR 1.03, 95% CI 0.79-1.35, P = 0.813). Subgroup analysis showed that HBV infection was positively associated with the risk of cervical cancer (OR 1.27, 95% CI 1.13-1.44, P = 0.000) in case-control studies. Asian women infected with HBV have a significantly increased risk of cervical cancer (OR 1.24, 95% CI 1.10-1.40, P = 0.001) and endometrial cancer (OR 1.46, 95% CI 1.07-1.99, P = 0.018). Hospital-based studies were found to be associated with an increased risk of cervical cancer (OR 1.30, 95% CI 1.14-1.47, P = 0.000) and endometrial cancer (OR 1.61, 95% CI 1.04-2.49, P = 0.032). The results of Begg's and Egger's tests showed no publication bias. CONCLUSIONS: This meta-analysis shows a positive association between HBV infection and cervical cancer. HBV is positively correlated with the risk of cervical cancer and endometrial cancer in Asian women and hospital-based populations. More multicenter prospective studies are required to confirm the findings.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.038 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".