Effects of Glycemic Index and Intake of Dietary Fiber on Serum HDL-Cholesterol Levels
Bibliographic record
Abstract
We previously studied effects of glycemic index (GI) and intake of dietary fiber on serum high-density lipoprotein (HDL)-C levels to make “Dietary Reference Intakes for Japanese 2015”, by using data obtained by clinical trials which evaluated effects of GI and intake of dietary fiber on HDL-C in Asian populations. We found that low GI and an increased intake of dietary fiber may be beneficially associated with HDL metabolism. Here we review meta-analyses on the effects of GI and intake of dietary fiber on serum HDL-C levels, to make “Dietary Reference Intake for Japanese 2020”. A search was conducted by using PubMed, Embase and Google Scholar, and the search period was comprised up to May 2018. In spite of significant associations of low GI and dietary fiber intake with reduction of low-density lipoprotein (LDL)-C, we could not observe any significant influences of low GI and dietary fiber intake on HDL metabolism. J Endocrinol Metab. 2018;8(4):57-61 doi: https://doi.org/10.14740/jem514w
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".