EFFECTS OF DIETARY FIBER ON HUMAN HEALTH: A REVIEW
Bibliographic record
Abstract
The aim of this article is to review literature data on the terminology, classification and physiological effects of dietary fibers. Results. The scientific studies on the terminology, classification and characteristics of various types of dietary fiber are presented, the sources of dietary fiber and their positive physiological effects are described. Nowadays no consensus definition has been given to the concept of dietary fibers. The authors point to dietary fibers being related to chemical compounds defined by structure, or functional properties, and/or a combination of both structural and functional properties. The authors noted one commonality in these definitions: each mentioned positive physiological effects. The modern classification system for dietary fibers is wide and diverse and can be based on origin, structure of polymers, solubility, ion exchange, sorption or physiological effect. Many studies have shown that dietary fibers can promote human health and help prevent specific chronic diseases that increase mortality and reduce life expectancy. Numerous healthful effects of the dietary fibers have been documented. These include curative and preventive effects for diseases such as obesity, certain types of cancers, cardiovascular diseases, diabetes, and constipation. Conclusion. Fibers are considered before other nutrients to ensure a healthy nutrition. Research continues to contribute new data on the effect of dietary fiber on the human body.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".