Role of Duodenogastroesophageal Reflux in the Pathogenesis of Esophageal Mucosal Injury and Gastroesophageal Reflux Symptoms
Bibliographic record
Abstract
BACKGROUND AND AIM: Patients with gastroesophageal reflux disease (GERD) usually suffer from acid reflux and duodenogastroesophageal reflux (DGER) simultaneously. The question of whether DGER has an important effect on the development of GERD remains controversial. The aim of the present study was to investigate the role of DGER in the pathogenesis of GERD and its value for the diagnosis of nonerosive reflux disease (NERD). METHODS: GERD was initially diagnosed using the reflux disease questionnaire. For further diagnosis, results of the upper gastrointestinal endoscopy (excluding a diagnosis of Barrett's esophagus) were considered in conjunction with simultaneous 24 h esophageal pH and bilirubin monitoring. RESULTS: According to endoscopic findings, 95 patients (43 men, 50+/-10 years of age) were divided into two groups: the reflux esophagitis (RE) group (n=51) and the NERD group (n=44). Three DGER parameters, the percentage of time with absorbance greater than 0.14, the total number of reflux episodes and the number of bile reflux episodes lasting longer than 5 min, were evaluated in the study. For the RE group, the values of the DGER parameters (19.05%+/-23.44%, 30.56+/-34.04 and 5.90+/-6.37, respectively) were significantly higher than those of the NERD group (7.26%+/-11.08%, 15.68+/-20.92 and 2.59+/-3.57, respectively, P<0.05 for all) but no significant difference was found in acid reflux. Of NERD patients, 18.5% were diagnosed with simple DGER. The positive diagnosis rate of NERD could be significantly elevated from 65.9% to 84.1% (P<0.05), if bilirubin monitoring was employed in diagnosis. CONCLUSIONS: DGER may occur independently but plays an important role in the development of RE and GERD symptoms. Simultaneous 24 h esophageal pH and bilirubin monitoring is superior to simple pH monitoring in helping identify patients at risk for NERD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".