Modulation of the microbial ecology of the human colon by probiotics, prebiotics and synbiotics to enhance human health: An overview of enabling science and potential applications
Bibliographic record
Abstract
In a young evolving science, there are always more questions than answers. That is also the situation in the emerging field of Probiotics, and this was made very clear at the International Probiotics Workshop in Amsterdam. In the report of this workshop, we present a selection of the most urgent questions in the field of probiotics. In addition, we propose a few strategies for the future of probiotics research. During the workshop, 120 experts--from disciplines including Human Nutrition, Gastroenterology, Nutritional Therapy, Cell Biology, Microbiology and Immunology--discussed new views on microbe-host interactions and the role of probiotics in prevention and alleviation of gastro-intestinal, atopic and auto-immune diseases. There is a general consensus among the experts that administering defined strains can help in preventing and curing gut flora related diseases: the first clinical trials show a promising role for probiotics. But the system is very complex, and most underlying mechanisms are still unclear. Rapid progress in this field will depend largely on the collaboration between fundamental researchers from different disciplines and medical specialists. Besides, more clinical studies are required to convince authorities and the public of the value of microbial therapies.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".