Nonerosive reflux disease: clinical concepts
Bibliographic record
Abstract
Esophageal symptoms can arise from gastroesophageal reflux disease (GERD) as well as other mucosal and motor processes, structural disease, and functional esophageal syndromes. GERD is the most common esophageal disorder, but diagnosis may not be straightforward when symptoms persist despite empiric acid suppressive therapy and when mucosal erosions are not seen on endoscopy (as for nonerosive reflux disease, NERD). Esophageal physiological tests (ambulatory pH or pH-impedance monitoring and manometry) can be of value in defining abnormal reflux burden and reflux-symptom association. NERD diagnosed on the basis of abnormal reflux burden on ambulatory reflux monitoring is associated with similar symptom response from antireflux therapy for erosive esophagitis. Acid suppression is the mainstay of therapy, and antireflux surgery has a definitive role in the management of persisting symptoms attributed to NERD, especially when the esophagogastric junction is compromised. Adjunctive approaches and complementary therapy may be of additional value in management. In this review, we describe the evaluation, diagnosis, differential diagnosis, and management of NERD.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".