Systematic Review of Fecal and Mucosa-Associated Microbiota Compositional Shifts in Colorectal Cancer
Bibliographic record
Abstract
Introduction: Gut microbiota is a major component of the intestinal luminal environment and plays important roles in colorectal cancer. Object: systematically review all the existing literature on the association of mucosa-associated and fecal microbiota with incidence, location, and stage of colorectal adenoma and carcinoma. Methods: The scientific search was done up to July 2018. The search was limited to the English language with predefined and proper keywords. Among 616 articles some of them were eliminated due to some reasons. The inclusion and exclusion criteria were defined. In the next step two reviewers (M.M and Z.K) independently scanned the titles of all retrieved articles, removed duplicates, and identified potentially relevant abstracts for further assessment. The Newcastle-Ottawa Scale (NOS) for assessing the Quality was used for quality control. Result: Finally, 54 articles were entered into the study. Fusobacteria 39 (72%), Firmicutes 22(40%), Bacteroidetes 20 (37%), Proteobacteria 15(27%), Actinobacteria 10(18%) was the most prevalent phylum which was found in colorectal cancer patients. Among these taxa some of them were increased in colorectal cancer patients compared to the control; on the other hand, some taxon was declined in colorectal cancer patients. Besides this, in some taxon there were controversies among articles. Conclusion: Early detection of CRC is essential because patients whose cancer are detected at an early stage have more chance of survival. Until now there are several studies have demonstrated the potential rule of gut microbiota to be used for detection of CRC, but there is not any predefining protocol for screening. Although we found lots of articles which were published in this area, for defining a precise microbiota profile we need large multicenter case-control studies, where can show the effect of most important confounding factors like nutrition, ethnicity, physical activity, smoking consumption, and genetic background.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.017 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".