Relationship between long‐term use of proton pump inhibitors and risk of gastric cancer: A systematic analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: This study aims to systematically analyze the effect of long-term therapy with proton pump inhibitors (PPIs) on the risk of gastric cancer. METHODS: PubMed, EMBASE, Cochrane Library, China National Knowledge Infrastructure (CNKI), and China biomedical literature database (CBM) were searched for studies before February 2019. We evaluated the quality of the included articles through the Newcastle-Ottawa Scale and gathered relevant data to calculate the pooled odds ratio (OR) through Stata14.0. RESULTS: Seven relevant articles conformed to the inclusion criteria; 943 070 patients were included. The pooled OR was 2.50; 95% CI (1.74, 3.85); the subgroup analysis results showed that patients who had used PPIs for more than 36 months were most likely to develop gastric cancer, and an increased risk was observed among patients after Helicobacter pylori eradication. Noncardia gastric cancer was more likely to develop. CONCLUSIONS: Long-term use of PPIs can possibly increase the risk of gastric cancer even among patients after H. pylori eradication; in particular, for noncardia gastric cancer, the risk increases with longer durations of PPI use. Due to the limited number of studies, more high-quality studies are required to be designed.
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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 | 0.006 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".