Cost effectiveness analysis of population-based serology screening and <sup>13</sup>C-Urea breath test for Helicobacter pylori to prevent gastric cancer: A markov model
Bibliographic record
Abstract
AIM: To compare the costs and effectiveness of no screening and no eradication therapy, the population-based Helicobacter pylori (H pylori) serology screening with eradication therapy and (13)C-Urea breath test (UBT) with eradication therapy. METHODS: A Markov model simulation was carried out in all 237900 Chinese males with age between 35 and 44 from the perspective of the public healthcare provider in Singapore. The main outcome measures were the costs, number of gastric cancer cases prevented, life years saved, and quality-adjusted life years (QALYs) gained from screening age to death. The uncertainty surrounding the cost-effectiveness ratio was addressed by one-way sensitivity analyses. RESULTS: Compared to no screening, the incremental cost-effectiveness ratio (ICER) was $16166 per life year saved or $13571 per QALY gained for the serology screening, and $38792 per life year saved and $32525 per QALY gained for the UBT. The ICER was $477079 per life year saved or $390337 per QALY gained for the UBT compared to the serology screening. The cost-effectiveness of serology screening over the UBT was robust to most parameters in the model. CONCLUSION: The population-based serology screening for H pylori was more cost-effective than the UBT in prevention of gastric cancer in Singapore Chinese males.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".