Association between Hepatitis B virus and gastric cancer: A systematic review and meta-analysis
Bibliographic record
Abstract
An increasing number of studies are suggesting that hepatitis B virus (HBV) infection may be associated with an increased risk of not only hepatocellular carcinoma but also gastric cancer (GC). Whether HBV infection can be a risk factor for GC remains to be explored. In this study, we systematically searched for all eligible literature in 7 databases (China National Knowledge Infrastructure, WanFang, China Science and Technology Journal, PubMed, Cochrane Library, Web of Science and Embase). Eligible studies were required to have a case-control or cohort design. Sixteen studies were included and a meta-analysis was performed using Stata version 17.0. The quality of the included studies was assessed using the Newcastle-Ottawa Scale. The association between HBV infection and risk of GC was quantified by calculating the odds ratio and 95% confidence interval. The proportion of high-quality studies was 87.5% (14/16). The risk of GC was higher when HBV infection was present than when it was not (combined odds ratio 1.29, 95% confidence interval 1.16–1.44; I2 = 62.7%, p < 0.001). The results of subgroup analyses were consistent with the main results. In conclusion, this systematic review and meta-analysis identified a positive association between HBV infection and an increased risk of GC.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.024 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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, 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".