Considerations for best practices in studies of fiber or other dietary components and the intestinal microbiome
Bibliographic record
Abstract
A 2-day workshop organized by the National Institutes of Health and U.S. Department of Agriculture included 16 presentations focused on the role of diet in alterations of the gastrointestinal microbiome, primarily that of the colon. Although thousands of research projects have been funded by U.S. federal agencies to study the intestinal microbiome of humans and a variety of animal models, only a minority addresses dietary effects, and a small subset is described in sufficient detail to allow reproduction of a study. Whereas there are standards being developed for many aspects of microbiome studies, such as sample collection, nucleic acid extraction, data handling, etc., none has been proposed for the dietary component; thus this workshop focused on the latter specific point. It is important to foster rigor in design and reproducibility of published studies to maintain high quality and enable designs that can be compared in systematic reviews. Speakers addressed the influence of the structure of the fermentable carbohydrate on the microbiota and the variables to consider in design of studies using animals, in vitro models, and human subjects. For all types of studies, strengths and weaknesses of various designs were highlighted, and for human studies, comparisons between controlled feeding and observational designs were discussed. Because of the lack of published, best-diet formulations for specific research questions, the main recommendation is to describe dietary ingredients and treatments in as much detail as possible to allow reproduction by other scientists.
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 | 0.692 | 0.655 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.007 | 0.024 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.017 | 0.018 |
| Research integrity | 0.026 | 0.040 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".