Microbiome Composition in Microscopic Colitis: A Systematic Review
Bibliographic record
Abstract
Believed to be a rare cause of chronic diarrhoea, microscopic colitis (MC) is a condition with rising incidence. Many prevalent risk factors and the unknown pathogenesis of MC rationalise the need for studies on microbiota composition. PubMed, Scopus, Web of Science and Embase were searched. Eight case-control studies were included. The risk of bias was assessed with the Newcastle–Ottawa Scale. Clinical details on the study population and MC were poor. The most consistent result among the studies was a decreased Akkermansia genus in faecal samples. Other results were inconsistent due to the different taxonomic levels of the outcomes. Possible changes in different taxa were observed in patients who suffered from MC compared to healthy controls. The alpha diversity compared between MC and the diarrhoea control may suggest potential similarities. The beta diversity in MC compared to healthy and diarrhoeal populations showed no significant outcomes. The microbiome composition in MC possibly differed from the healthy control, but no agreement regarding taxa was made. It might be relevant to focus on possible factors influencing the microbiome composition and its relationship with other diarrhoeal diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".