Proton pump inhibitor and histamine‐2 receptor antagonist use and risk of liver cancer in two population‐based studies
Bibliographic record
Abstract
BACKGROUND: Proton pump inhibitors (PPIs) and histamine-2 receptor antagonists (H2RAs) are commonly used. PPIs have been shown to promote liver cancer in rats; however, only one study has examined the association in humans. AIMS: To investigate PPIs and H2RAs and risk of primary liver cancer in two large independent study populations. METHODS: We conducted a nested case-control study within the Primary Care Clinical Informatics Unit (PCCIU) database in which up to five controls were matched to cases with primary liver cancer, recorded by General Practitioners. Odds ratios (ORs) and 95% confidence intervals (95% CIs) for associations with prescribed PPIs and H2RAs were calculated using conditional logistic regression. We also conducted a prospective cohort study within the UK Biobank using self-reported medication use and cancer-registry recorded primary liver cancer. Hazard ratios (HRs) and 95% CIs were calculated using Cox regression. RESULTS: In the PCCIU case-control analysis, 434 liver cancer cases were matched to 2103 controls. In the UK Biobank cohort, 182 of 475 768 participants developed liver cancer. In both, ever use of PPIs was associated with increased liver cancer risk (adjusted OR 1.80, 95% CI 1.34, 2.41 and adjusted HR 1.99, 95% CI 1.34, 2.94 respectively). There was little evidence of association with H2RA use (adjusted OR 1.21, 95% CI 0.84, 1.76 and adjusted HR 1.70, 95% CI 0.82, 3.53 respectively). CONCLUSIONS: We found some evidence that PPI use was associated with liver cancer. Whether this association is causal or reflects residual confounding or reverse causation requires additional research.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".