Asia‐Pacific consensus on the management of gastroesophageal reflux disease: Update
Bibliographic record
Abstract
BACKGROUND AND AIMS: Since the publication of the Asia-Pacific GERD consensus in 2004, more data concerning the epidemiology and management of gastroesophageal reflux disease (GERD) have emerged. An evidence based review and update was needed. METHODS: A multidisciplinary group developed consensus statements using the Delphi approach. Relevant data were presented, and the quality of evidence, strength of recommendation, and level of consensus were graded. RESULTS: GERD is increasing in frequency in Asia. Risk factors include older age, male sex, race, family history, higher socioeconomic status, increased body mass index, and smoking. Symptomatic response to a proton pump inhibitor (PPI) test is diagnostic in patients with typical symptoms if alarm symptoms are absent. A negative pH study off therapy excludes GERD if a PPI test fails. The role for narrow band imaging, capsule endoscopy, and wireless pH monitoring has not yet been undefined. Diagnostic strategies in Asia must consider coexistent gastric cancer and peptic ulcer. Weight loss and elevation of head of bed improve reflux symptoms. PPIs are the most effective medical treatment. On-demand therapy is appropriate for nonerosive reflux disease (NERD) patients. Patients with chronic cough, laryngitis, and typical GERD symptoms should be offered twice daily PPI therapy after excluding non-GERD etiologies. Fundoplication could be offered to GERD patients when an experienced surgeon is available. Endoscopic treatment of GERD should not be offered outside clinical trials. CONCLUSIONS: Further studies are needed to clarify the role of newer diagnostic modalities and endoscopic therapy. Diagnostic strategies for GERD in Asia must consider coexistent gastric cancer and peptic ulcer. PPIs remain the cornerstone of therapy.
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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 | 0.018 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".