Diagnosis and Management of Non-Erosive Reflux Disease – The Vevey NERD Consensus Group
Bibliographic record
Abstract
BACKGROUND/AIMS: Although considerable information exists regarding gastroesophageal reflux disease with erosions, much less is known of non-erosive reflux disease (NERD), the dominant form of reflux disease in the developed world. METHODS: An expert international group using the modified Delphi technique examined the quality of evidence and established levels of agreement relating to different aspects of NERD. Discussion focused on clinical presentation, assessment of clinical outcome, pathobiological mechanisms, and clinical strategies for diagnosis and management. RESULTS: Consensus was reached on 85 specific statements. NERD was defined as a condition with reflux symptoms in the absence of mucosal lesions or breaks detected by conventional endoscopy, and without prior effective acid-suppressive therapy. Evidence supporting this diagnosis included: responsiveness to acid suppression therapy, abnormal reflux monitoring or the identification of specific novel endoscopic and histological findings. Functional heartburn was considered a separate entity not related to acid reflux. Proton pump inhibitors are the definitive therapy for NERD, with efficacy best evaluated by validated quality-of-life instruments. Adjunctive antacids or H(2) receptor antagonists are ineffective, surgery seldom indicated. CONCLUSIONS: Little is known of the pathobiology of NERD. Further elucidation of the mechanisms of mucosal and visceral hypersensitivity is required to improve NERD management.
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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 | 0.039 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".