Cost-effectiveness analysis of <i>Helicobacter pylori</i> screening in prevention of gastric cancer in Chinese
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to evaluate the costs and effectiveness associated with no screening, Helicobacter pylori serology screening, and the 13C-urea breath test (UBT) for gastric cancer in the Chinese population. METHODS: A Markov model simulation was carried out in Singaporean Chinese at 40 years of age (n = 478,500) from the perspective of public healthcare providers. The main outcome measures were costs, number of gastric cancer cases prevented, life-years saved, quality-adjusted life-years (QALYs) gained from the screening age to death, and incremental cost-effectiveness ratios (ICERs), which were compared among the three strategies. The uncertainty surrounding ICERs was addressed by scenario analyses and probabilistic sensitivity analysis using Monte Carlo simulation. RESULTS: The ICER of serology screening versus no screening was $25,881 per QALY gained (95 percent confidence interval (95 percent CI), $5,700 to $120,000). The ICER of UBT versus no screening was $53,602 per QALY gained (95 percent CI, $16,000 to $230,000). ICER of UBT versus serology screening was $470,000 per QALY gained, for which almost all random samples of the ICERs distributed above $50,000 per QALY. CONCLUSIONS: It cannot be confidently concluded that either H pylori screening was a cost-effective strategy compared with no screening in all Chinese at the age of 40 years. Nevertheless, serology screening has demonstrated much more potential to be a cost-effective strategy, especially in the population with higher gastric cancer prevalence.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".