A Critical Assessment of the Current Status of Non-Erosive Reflux Disease
Bibliographic record
Abstract
Non-erosive reflux disease (NERD) has assumed increasing prominence in studies of gastroesophageal reflux disease (GERD), but it remains a challenge to define NERD precisely and to define its place in the investigation and treatment of GERD. Most simply, NERD may be defined as GERD in an individual who has no evidence of erosions at endoscopy. Unfortunately, the characteristic symptoms of GERD--heartburn and regurgitation--are insufficient to identify all GERD patients and, hence, the diagnosis of NERD is hampered by the lack of clear criteria for the symptomatic diagnosis of GERD. The diagnosis of NERD is hampered further by limited interobserver agreement on the endoscopic diagnosis of erosive esophagitis and by the fact that endoscopy is often performed soon after patients have discontinued therapy. Improvements in endoscopic technology will increase the likelihood of identifying small erosions or other reflux-related lesions; however, this will increase the proportion of patients considered to have erosive esophagitis without defining precisely what constitutes NERD. It is important to recognize that NERD is but one manifestation of GERD and that it, like other manifestations of GERD, is associated with a marked diminution in patients' quality of life. However, this recognition apart, there seems to be little practical benefit or understanding to be gained in clinical practice or clinical research from considering NERD as a distinct entity or from studying NERD patients in isolation. Advances in understanding the pathogenesis of GERD and its symptoms may be better served by categorizing GERD with respect to the spectrum of its histologic, functional, endoscopic and symptomatic manifestations rather than by studying NERD, a manifestation that is characterized solely by the absence of esophageal erosions.
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 | 0.018 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".