Gut Microbiota Association with Diverticular Disease Pathogenesis and Progression: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Growing evidence supports the role of the intestinal microbiome in the development of different intestinal and extraintestinal diseases. Diverticular disease (DD) is one of the most common disorders in western countries. In the last years, different articles have suggested a possible role of the intestinal microbiome in DD pathogenesis and in the development of acute diverticulitis (AD). This systematic review aimed to clarify the current knowledge on the role of the intestinal microbiome in colonic diverticulitis in different stages according to the 2009 PRISMA guidelines. MATERIALS AND METHODS: Two independent reviewers searched the literature in a systematic manner through online databases, including Medline, Scopus, Embase, Cochrane Oral Health Group Specialized Register, ProQuest Dissertations and Theses Database, and Google Scholar. Patients with any stage of disease were included. The Newcastle-Ottawa scale for case-control and cohort studies was used for the quality assessment of the selected articles. RESULTS: Overall, nine studies were included in the review. Only one article was focused on patients with AD, while all other articles only considered patients with DD without acute inflammation signs. Enterobacteriaceae seems to be the microbiota most associated with the disease, followed by Bifidobacteria. CONCLUSIONS: All the included studies showed great heterogeneity in population characteristics and sampling methods. Therefore, given the high prevalence of colonic diverticulitis in the general population, further studies are needed to clarify the role of the intestinal microbiome, paving the way to new target therapies with important social implications.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".