Esophageal Intraepithelial Neutrophil Infiltration is Common in Nigerian Patients With Non-Erosive Reflux Disease
Bibliographic record
Abstract
BACKGROUND: Non-erosive reflux disease (NERD) is a variant of gastroesophageal reflux disease (GERD) in which patients with typical reflux symptoms have no evidence of erosive esophagitis at endoscopy. An objective diagnostic tool for NERD remains an unmet need for clinicians and researchers. This study was designed to determine the types of histological alterations seen in Nigerian patients with NERD. METHODS: This was a prospective cross-sectional study in which mucosal biopsy was taken from the lower esophagus in patients with NERD. Similar biopsy was also taken from patients with nonulcer dyspepsia who served as controls. The materials were processed and examined histologically. RESULTS: There were 68 patients with NERD and 60 patients with nonulcer dyspepsia. Intraepithelial neutrophil infiltration was significantly more frequent in patients with NERD compared to those with nonulcer dyspepsia (47.1% vs 13.3%, P = 0.0326). Epithelial proliferative chnges in the form of basal cell hyperplasia and papilla elongation were minimal (11.8% and 3.3% respectively). CONCLUSIONS: Nigerian patients with NERD have a high degree of esophageal intraepithelial neutrophil infiltration and a low prevalence of epithelial proliferative changes. This may be related to the relative rarity of Barrett's esophagus in Nigerians.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".