Health economics evaluation of diagnostic strategies for gastro-oesophageal reflux disease with reflux symptoms in China: a modelling study
Bibliographic record
Abstract
OBJECTIVES: American College of Gastroenterology (ACG) and Chinese expert consensus recommended different algorithmic approaches for the diagnosis of gastro-oesophageal reflux disease (GERD) are not yet defined. We compared the two recommended diagnostic processes using a Chinese population-based health economics analysis. METHODS: Our analysis considered a hypothetical cohort of patients with typical reflux symptoms. We constructed a decision tree model to compare the two recommended diagnostic processes described in ACG clinical guidelines (stratified endoscopy strategy) and Chinese expert consensus (endoscopy-first strategy). The first strategy begins with hazard stratification based on alarm symptoms. Patients with alarm symptoms directly undergo endoscopic examination, while patients without alarm symptoms receive proton pump inhibitors as diagnostic treatment. In the second strategy, all patients with reflux symptoms complete an endoscopic examination. Sensitivity analysis was performed to evaluate a range of cost and probability estimates on costs and health outcomes over a 1-year time horizon from the healthcare system perspective. RESULTS: The total expected costs were US$122.51 for the stratified endoscopy strategy and US$150.12 for the endoscopy-first strategy. The incremental cost-effectiveness ratio (ICER) comparing the endoscopy-first strategy with the stratified endoscopy strategy was US$440.39 per additional correct case of GERD. The rates of detecting upper gastrointestinal carcinoma of the two strategies were 0.0088 and 0.0120, and the ICER was US$8561.34. CONCLUSIONS: The use of endoscopy for all patients with reflux symptoms was more effective but with an increased cost compared with the strategy recommended in international guidelines.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".