Non-Erosive Reflux Disease – Defining the Entity and Delineating the Management
Bibliographic record
Abstract
In the developed world, most patients with gastroesophageal reflux disease (GERD) do not exhibit erosions when examined by standard white light endoscopy. Despite the high prevalence of such non-erosive reflux disease (NERD), relatively little is known of its underlying pathophysiology, hence there is no clear guide to clinical management. To establish areas of agreement or uncertainty in NERD, an international meeting was held in Vevey, Switzerland, in late 2007. The goal was to document current thinking in the areas of clinical presentation, assessment of clinical outcome, pathobiological mechanisms, and define optimal clinical strategies to diagnose and manage NERD. After extensive debates, the modified Delphi technique was utilized to reach a consensus on 85 specific statements. In addition, it was proposed that NERD be defined as 'a subcategory of GERD characterised by troublesome reflux-related symptoms in the absence of esophageal mucosal erosions/breaks at conventional endoscopy and without recent acid suppressive therapy'. Evidence in support of this diagnosis may include responsiveness to acid suppression therapy, abnormal reflux monitoring or the identification of specific novel endoscopic findings. Defining the current state of knowledge in NERD should help improve the elucidation and management of this condition in the future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".