The role of histology in the diagnosis of non‐erosive reflux disease: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Non-erosive reflux disease (NERD) accounts for over half of all gastroesophageal reflux cases and is characterized by reflux symptoms with pathologic acid exposure on pH monitoring but no evidence of erosions on upper endoscopy. Ambulatory pH monitoring is limited by availability and patient tolerance. The utility of performing esophageal mucosal biopsies in diagnosing NERD is unclear. We conducted a systematic review and meta-analysis to determine the sensitivity of esophageal mucosal biopsies in diagnosing NERD. METHODS: Data were obtained from Embase and Ovid MEDLINE from inception to April 2021. Studies were included if esophageal mucosal biopsies were taken and analyzed using conventional histopathologic analysis in symptomatic NERD patients. Relevant data was including histologic abnormalities and location of the biopsy. Sensitivity and specificity were calculated against healthy controls or those with functional heartburn. RESULTS: The search yielded 2871 studies, of which 10 studies met our inclusion criteria and contained raw data. Histological abnormalities included histologic sum scores, papillary elongation, basal cell hyperplasia, and dilated intraepithelial spaces. When assessing for the presence of any abnormality, biopsies taken <3 cm from the lower esophageal sphincter (LES) had a pooled sensitivity of 0.71 (95% CI 0.64-0.77) and specificity of 0.64 (95% 0.54-0.73); however, analysis of individual histologic features such as the presence of eosinophils improved the sensitivity. CONCLUSIONS: Although esophageal mucosal biopsies had poor sensitivity at diagnosing NERD, biopsies taken within 3 cm of the LES had higher sensitivity when pathologists reported upon eosinophils and dilated intraepithelial spaces.
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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 | 0.018 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.031 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".