Cost-Effectiveness Analysis of Hp and New Gastric Cancer Screening Scoring System for Screening and Prevention of Gastric Cancer
Bibliographic record
Abstract
Gastric cancer is one of the most common gastrointestinal cancers. Early diagnosis can improve the 5-year survival rate. This study aimed to evaluate the cost-effectiveness of Helicobacter pylori (Hp) and a new gastric cancer screening scoring system (NGCS) in areas with a high incidence of gastric cancer. A decision-analytic Markov model was constructed based on the theory and method of cost-effectiveness analysis, which included three decisions: no screening, Hp screening, and NGCS screening. The uncertainty of each parameter in the model was determined using a one-way sensitivity analysis and probability sensitivity analysis. The results of the cost-effectiveness analysis revealed that the application of the NGCS had the highest cost-effectiveness, while the one-way sensitivity analysis revealed that the probability of intestinal metaplasia progression to dysplasia had the most significant effect on the incremental cost-effectiveness ratio. The probability sensitivity analysis concluded that the result of the NGCS having the highest cost-effectiveness was stable. Although the application of the NGCS will require upfront screening costs, it can significantly improve the detection rate of early gastric cancer and save the consequent long-term healthcare costs. It is practicable and can be popularized in China.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".