Noninvasive, MultiOmic, and Multicompartmental Biomarkers of Reflux Disease: A Systematic Review
Bibliographic record
Abstract
Background and Aims Gastroesophageal reflux disease (GERD) is a prevalent gastrointestinal disorder that may complicate conditions such as obstructive airway disease. Our group has identified predictive biomarkers of GERD in particulate exposed first responders with obstructive airway disease. In addition, GERD diagnosis and treatment is costly and invasive. In light of these clinical concerns, we aimed to systematically review studies identifying noninvasive, multiOmic, and multicompartmental biomarkers of GERD. Methods A systematic review of PubMed and Embase was performed using keywords focusing on reflux disease and biomarkers and registered with PROSPERO. We included original human studies in English, articles focusing on noninvasive biomarkers of GERD published after December 31, 2009. GERD subtypes (non-erosive reflux disease and erosive esophagitis) and related conditions (Barrett's Esophagus [BE] and Esophageal Adenocarcinoma). Predictive measures were synthesized and risk of bias assessed (Newcastle-Ottawa Scale). Results Initial search identified n = 238 studies and n = 13 articles remained after applying inclusion/exclusion criteria. Salivary pepsin was the most studied biomarker with significant sensitivity and specificity for GERD. Serum assessment showed elevated levels of Tumor Necrosis Factor- alpha in both GERD and Barrett's. Exhaled breath volatile sulfur compounds and acetic acid were associated with GERD. Oral Microbiome: Models with Lautropia , Streptococcus , and Bacteroidetes showed the greatest discrimination between BE and controls vs Lautropia ; ROC AUC 0.94 (95% confidence interval; 0.85–1.00). Conclusion Prior studies identified significant multiOmic, multicompartmental noninvasive biomarker risks for GERD and BE. However, studies have a high risk of bias and the reliability and accuracy of the biomarkers identified are greatly limited, which further highlights the need to discover and validate clinically relevant noninvasive biomarkers of GERD.
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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 | 0.013 | 0.049 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.007 |
| Bibliometrics | 0.017 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".