Hepatitis B virus status and the risk of pancreatic cancer
Bibliographic record
Abstract
OBJECTIVE: Whether hepatitis B virus (HBV) infection increases the risk of pancreatic cancer (PaC) is controversial. We carried out a meta-analysis to evaluate the association between HBV status and the risk of PaC. METHODS: PubMed, Embase, and the China National Knowledge Infrastructure were searched from their inception through April 2012 for case-control and cohort studies that have reported an association between HBV status and the risk of PaC. The reference lists of pertinent publications were also reviewed for potential studies. Methodological quality was assessed using the Newcastle-Ottawa Quality Assessment Scale. A random-effects model was used to summarize odd ratios (ORs) and 95% confidence intervals (CIs). RESULTS: We included seven case-control studies and three cohort studies, involving 5883 PaC cases. The summary OR of developing PaC was 1.22 (95% CI: 0.90-1.67) for individuals who were HBV surface antigen (HBsAg)-positive. Compared with the individuals who were never exposed to HBV infection, the summary OR of the risk of PaC was 1.60 (95% CI: 1.26-2.05) for chronic or inactive HBsAg carriers (HBsAg-positive) and 1.76 (95% CI: 1.05-2.93) for anti-HBc-positive but anti-HBs-negative individuals. CONCLUSION: Inactive HBsAg carrier status and possible occult HBV infection may increase the risk of PaC. Large population-based multicenter prospective studies are required to further confirm this finding.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.012 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".