A Second-Order Simulation Model of the Cost-Effectiveness of Managing Dyspepsia in the United States
Bibliographic record
Abstract
BACKGROUND: The "gold-standard'' evidence of effectiveness for a clinical practice guideline is the randomized controlled trial (RCT), although RCTs have a limited ability to explore potential management strategies for a chronic disease where these interact over time. Modeling can be used to fill this gap, and models have become increasingly complex, with both dynamic sampling and representation of second-order uncertainty to provide more precise estimates. However, both simulation modeling and probabilistic sensitivity analysis are rarely used together. The objective of this study was to explore uncertainty in controversial areas of the 2005 American Gastroenterology Association position statement on the management of dyspepsia. METHODS: Individual sampling model, incorporating a second-order probabilistic sensitivity analysis. POPULATION: US adult patients presenting in primary care with dyspepsia. Interventions compared: empirical acid suppression, test and treat for Helicobacter pylori, initial endoscopy, acid suppression then endoscopy, test and treat then proton pump inhibitor (PPI) then endoscopy. OUTCOMES: Cost-effectiveness, quality-adjusted life years, and costs in US dollars from a societal perspective, measured over a 5-year period. DATA SOURCES: mainly Cochrane meta-analyses. RESULTS: Endoscopy was dominated at all ages by other strategies. PPI therapy was the most cost-effective strategy in 30-year-olds with a low prevalence of H. pylori. In 60-year-olds, H. pylori test and treat was the most cost-effective option. CONCLUSIONS: Acid suppression alone was more cost-effective than either endoscopy or H. pylori test and treat in younger dyspepsia patients with a low prevalence of infection.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".