A population-based survey of the epidemiology of symptom-defined gastroesophageal reflux disease: the Systematic Investigation of Gastrointestinal Diseases in China
Bibliographic record
Abstract
BACKGROUND: The epidemiology of gastroesophageal reflux disease (GERD) has yet to be investigated using the symptomatic threshold criteria recommended by the Montreal Definition. This study aimed to determine the prevalence of symptom-defined GERD across five regions of China, and to investigate variables associated with GERD. METHODS: A representative sample of 18 000 adults (aged 18-80 years) were selected equally from rural and urban areas in each region (n = 1800). According to the Montreal Definition, GERD is present when mild symptoms of heartburn and/or regurgitation occur on >or=2 days a week, or moderate-to-severe symptoms of heartburn and/or regurgitation occur on >or=1 day a week. RESULTS: In total, 16 091 participants completed the survey (response rate: 89.4%) and 16 078 responses were suitable for analysis. Applying the Montreal criteria, the prevalence of symptom-defined GERD was 3.1% and varied significantly (p < 0.001) among the five regions (from 1.7% in Guangzhou to 5.1% in Wuhan) and between rural and urban populations (3.8% vs 2.4%). Factors significantly associated with GERD included living in a rural area and a family history of gastrointestinal diseases. CONCLUSIONS: This population-based survey found that the prevalence of symptom-defined GERD in China was 3.1%, which is lower than that found in Western countries.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".