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Record W4412403393 · doi:10.1097/md.0000000000043053

A meta-analysis of the effects of proton pump inhibitors on the risk of gastric cancer

2025· review· en· W4412403393 on OpenAlexaboutno aff
Hao Zhang, Xianyu Meng, Dongyun Gou, Xiaojing Liu, Yu Huang, Siyu Liu, Haiyang Wang, Hongyan Li

Bibliographic record

VenueMedicine · 2025
Typereview
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHelicobacter pyloriCochrane LibraryCancerInternal medicineIncidence (geometry)GERDProton-pump inhibitorGastroenterologyMeta-analysisDiseaseOncologyReflux

Abstract

fetched live from OpenAlex

BACKGROUND: Proton pump inhibitors (PPIs) are commonly prescribed drugs in clinical practice, mainly for the treatment of Helicobacter pylori infection and gastroesophageal reflux disease (GERD). With the application of PPIs, doctors have found a variety of adverse reactions related to it, with gastric cancer being the most serious. Our aim is to investigate whether the use of PPIs increases the probability of gastric cancer. METHODS: By searching PubMed, EMBASE, the Cochrane Library and Web of science, references related to PPIs and gastric cancer were selected, and Newcastle-Ottawa scale (NOS) was used to evaluate the quality of the included references and analyze their bias. Then stataSE-64 was used for statistical analysis. The above processes were independently searched and evaluated by 2 researchers. RESULTS: The use of PPIs significantly increased the incidence of gastric cancer (RR = 1.75, 95% CI: 1.48-2.07, P = .000). CONCLUSION: Long-term use of PPIs may increase the incidence of gastric cancer.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.022
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0140.031
Bibliometrics0.0040.005
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.064
GPT teacher head0.369
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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