Perspective on the health value of carbohydrate-rich foods: glycemic index and load; fiber and whole grains
Bibliographic record
Abstract
BACKGROUND: For over 45 y increasingly comprehensive food tables of glycemic index (GI) and glycemic load (GL) have been published in the American Journal of Clinical Nutrition to determine the GI and GL values of diets. Recently the WHO based on a 2019 series of meta-analyses concluded that increases in dietary fiber and whole grains but not reduction in GI or GL warranted recommendations for chronic disease reduction. METHODS AND RESULTS: We therefore provide a perspective on the current evidence that indicates that GI and GL are also determinants of risk of chronic disease outcomes. We are also concerned with the term dietary fiber used in the singular when there are many dietary fibers that may differ in their physiological effects. Furthermore, the term "whole grains" that refers to "whole grain flour" limits the exploration of "intact" grains that are low GI and have useful physiological effects. CONCLUSION: We conclude that all these determinants of the health values of carbohydrate-rich foods should be used in combination to assess the health value of carbohydrate-rich foods.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".