Gut microbial metabolism and colon cancer: Can manipulations of the microbiota be useful in the management of gastrointestinal health?
Bibliographic record
Abstract
The gut microbiota is an important component of the human body and its immune-modulating and metabolic activities are critical to maintain good health. Gut microbes, however, are sensitive to changes in diet, exposure to antibiotics, or infections, all of which cause transient disruptions in the microbial composition, a phenomenon known as dysbiosis. It is now recognized that microbial dysbiosis is at the root of many gastrointestinal disorders. However, the mechanisms through which bacterial dysbiosis initiates disease are not fully understood. Microbially-derived metabolites and their role in disease have also attracted significant attention. Identification of cancer-associated bacteria and understanding the contributions of microbial metabolism in health and disease are exciting but challenging areas that will allow defining microbial biomarkers for predicting gastrointestinal disorders. Understanding the complex interactions between gut microbiota, diet, host immune system and host genetics will be critical to developing more personalized therapies and approaches to treat 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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".