The Gut Microbiome in Inflammatory Bowel Disease: Lessons Learned From Other Immune-Mediated Inflammatory Diseases
Bibliographic record
Abstract
There is a growing appreciation for the role of the gut microbiome in human health and disease. Aided by advances in sequencing technologies and analytical methods, recent research has shown the healthy gut microbiome to possess considerable diversity and functional capacity. Dysbiosis of the gut microbiota is believed to be involved in the pathogenesis of not only diseases that primarily affect the gastrointestinal tract but also other less obvious diseases, including neurologic, rheumatologic, metabolic, hepatic, and other illnesses. Chronic immune-mediated inflammatory diseases (IMIDs) represent a group of diseases that share many underlying etiological factors including genetics, aberrant immunological responses, and environmental factors. Gut dysbiosis has been reported to be common to IMIDs as a whole, and much effort is currently being directed toward elucidating microbiome-mediated disease mechanisms and their implications for causality. In this review, we discuss gut microbiome studies in several IMIDs and show how these studies can inform our understanding of the role of the gut microbiome in inflammatory bowel disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".