A Study on the Relationship among Dietary Fiber Intake, Type 2 Diabetes, Microbiota and Immune System
Bibliographic record
Abstract
With rapid socioeconomic development and demographic changes, the global diabetes mellitus pandemic becomes an alarming problem. It is necessary to extenuate the incidence of diabetes mellitus and discover potential effective treatments. Dietary fiber (DF) takes an important place in a healthy diet and they are mainly present in plant-based foods, such as vegetables, nuts, and beans. The global dietary fiber consumption trend is projected to continuously increase as the public became aware of its importance. Recent clinical trials indicated that the amount of dietary fiber was correlated with the Type 2 Diabetes Mellitus (T2DM) rate. In the current research, an underlying mechanism will be investigated. Several groups proved that dietary fiber intake could influence the diversity of intestinal microbiota and a decrease in microbiota composition could further affect the level of inflammation in the human immune system. Other studies also reflected that both the composition of gastrointestinal microflora and inflammation level was associated with the incidence of T2DM. The finding suggested a lower level of inflammation tended to have a lower rate of T2DM. Hence, the level of dietary fiber intake could eventually have an impact on T2DM incidence.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".