Update on the epidemiology of gastro-oesophageal reflux disease: a systematic review
Bibliographic record
Abstract
OBJECTIVE: To update the findings of the 2005 systematic review of population-based studies assessing the epidemiology of gastro-oesophageal reflux disease (GERD). DESIGN: PubMed and Embase were screened for new references using the original search strings. Studies were required to be population-based, to include ≥ 200 individuals, to have response rates ≥ 50% and recall periods <12 months. GERD was defined as heartburn and/or regurgitation on at least 1 day a week, or according to the Montreal definition, or diagnosed by a clinician. Temporal and geographic trends in disease prevalence were examined using a Poisson regression model. RESULTS: 16 studies of GERD epidemiology published since the original review were found to be suitable for inclusion (15 reporting prevalence and one reporting incidence), and were added to the 13 prevalence and two incidence studies found previously. The range of GERD prevalence estimates was 18.1%-27.8% in North America, 8.8%-25.9% in Europe, 2.5%-7.8% in East Asia, 8.7%-33.1% in the Middle East, 11.6% in Australia and 23.0% in South America. Incidence per 1000 person-years was approximately 5 in the overall UK and US populations, and 0.84 in paediatric patients aged 1-17 years in the UK. Evidence suggests an increase in GERD prevalence since 1995 (p<0.0001), particularly in North America and East Asia. CONCLUSIONS: GERD is prevalent worldwide, and disease burden may be increasing. Prevalence estimates show considerable geographic variation, but only East Asia shows estimates consistently lower than 10%.
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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 | 0.024 | 0.077 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.027 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".