Mechanisms Underlying Food-Triggered Symptoms in Disorders of Gut-Brain Interactions
Bibliographic record
Abstract
There has been a dramatic increase in clinical studies examining the relationship between disorders of gut-brain interactions and symptoms evoked by food ingestion in the upper and lower gastrointestinal tract, but study design is challenging to verify valid endpoints. Consequently, mechanistic studies demonstrating biological relevance, biomarkers and novel therapeutic targets are greatly needed. This review highlights emerging mechanisms related to nutrient sensing and tasting, maldigestion, physical effects with underlying visceral hypersensitivity, allergy and immune mechanisms, food-microbiota interactions and gut-brain signaling, with a focus on patients with functional dyspepsia and irritable bowel syndrome. Many patients suffering from disorders of gut-brain interactions exhibit these mechanism(s) but which ones and which specific properties may vary widely from patient to patient. Thus, in addition to identifying these mechanisms and the need for further studies, biomarkers and novel therapeutic targets are identified that could enable enriched patient groups to be studied in future clinical trials examining the role of food in the generation of gut and non-gut symptoms.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".