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Record W4411243595 · doi:10.2196/72228

The Cost-Effectiveness of Adding Helicobacter Pylori Screening to the National Gastric Cancer Screening Program in Korea: Protocol for a Cost-Effectiveness Modeling Study

2025· article· en· W4411243595 on OpenAlexvenueno aff
Seowoo Bae, Joon Sung Kim, Moon Won Lee, Gwang Ha Kim, Young‐Il Kim, Woon Tae Jung, Gwang Ho Baik, Beom Jin Kim, Joongyub Lee, Mina Suh, Jae Gyu Kim

Bibliographic record

VenueJMIR Research Protocols · 2025
Typearticle
Languageen
FieldMedicine
TopicHelicobacter pylori-related gastroenterology studies
Canadian institutionsnot available
Fundersnot available
KeywordsHelicobacter pyloriMedicineCancerCancer screeningCost-effectiveness analysisCost effectivenessQuality-adjusted life yearIncremental cost-effectiveness ratioCancer preventionInternal medicineRisk analysis (engineering)

Abstract

fetched live from OpenAlex

BACKGROUND: In Korea, the National Cancer Screening Program (NCSP) was implemented in 1999 and provides biennial endoscopy for adults aged ≥40 years. The NCSP has contributed to the early detection of gastric cancer and reduction of associated mortality in Korea. Helicobacter pylori is the main cause of gastric cancer. Screening for and eradication of H pylori reduces the incidence and mortality of gastric cancer. Previous studies have reported that screening for H pylori is a cost-saving intervention that can significantly decrease gastric cancer burden in areas with a high prevalence of H pylori infection. However, no study has examined whether incorporating H pylori screening into national endoscopic screening is cost effective. OBJECTIVE: This study aims to evaluate the cost-effectiveness of incorporating H pylori screening into Korea's National Gastric Cancer Screening Program. METHODS: We have developed a Markov model to compare two strategies: (1) endoscopy screening every 2 years starting at the age of 40 years (conventional screening), and (2) H pylori screening at the age of 40 years followed by continuous endoscopy screening every 2 years. We will also conduct a comparative analysis by varying the age at which the H pylori screening is performed. The primary outcome is the incremental cost-utility ratio (ICUR), calculated by dividing the incremental cost by the incremental quality-adjusted life-years (QALYs) between the two strategies. A probabilistic sensitivity analysis will be performed to test the uncertainty of the cost-effectiveness results. A sensitivity analysis will identify the most influential variables for cost-effectiveness. RESULTS: The primary outcome parameter is the cost-effectiveness of adding H pylori testing to the current NCSP, which is expressed as the ICUR. Costs and utilities are discounted at an annual rate of 4.5%. The ICUR threshold is set at KRW 50 million (US $36,719), which is the South Korean gross domestic product per capita. This research has been funded by a Patient-Centered Clinical Research Coordinating Center grant from the Ministry of Health & Welfare, Republic of Korea (grant RS-2024-00398474). This study will analyze and synthesize previously published information and is thus exempt from institutional review board approval. Data collection started in June 2024 and was completed in May 2025. Study results will be published in peer-reviewed journals and presented at national and international conferences throughout 2025. CONCLUSIONS: We will examine whether introducing H pylori testing and eradication therapy into the NCSP is a more cost-effective strategy for reducing gastric cancer risk than conventional endoscopy-based screening. Our study also examines the optimal age for H pylori screening, as well as the optimal screening frequency. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/72228.

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)0.030
metaresearch head score (Gemma)0.047
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.047
Threshold uncertainty score0.159

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0300.047
Meta-epidemiology (narrow)0.0050.002
Meta-epidemiology (broad)0.0050.015
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0470.003

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 head0.316
GPT teacher head0.568
Teacher spread0.252 · 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 designSimulation or modeling
Domainnot available
GenreProtocol

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

Citations3
Published2025
Admission routes1
Has abstractyes

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