Effects of Antibiotics on Human Microbiota and Subsequent Disease
Bibliographic record
Abstract
Although antibiotics have significantly improved human health and life expectancy, their disruption of the existing microbiota has been linked to significant side effects such as antibiotic-associated diarrhea, pseudomembranous colitis, and increased susceptibility to subsequent disease. By using antibiotics to break colonization resistance against Clostridium, Salmonella, and Citrobacter species, researchers are now exploring mechanisms for microbiota-mediated modulation against pathogenic infection, revealing potential roles for different phyla and family members as well as microbiota-liberated sugars, hormones, and short-chain fatty acids in regulating pathogenicity. Furthermore, connections are now being made between microbiota dysbiosis and a variety of different diseases such as rheumatoid arthritis, inflammatory bowel disease, type 1 diabetes, atopy, and obesity. Future advances in the rapidly developing field of microbial bioinformatics will enable researchers to further characterize the mechanisms of microbiota modulation of disease and potentially identify novel therapeutics against 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".