Comparative effects of different whole grains and brans on blood lipid: a network meta-analysis
Bibliographic record
Abstract
PURPOSE: The comparative effects of different whole grains and brans on blood lipid are still not totally elucidated. We aimed to estimate and rank the effects of different whole grains and brans on the control of blood lipid. METHODS: We performed a strategic literature search of PubMed, EMBASE and the Cochrane Library for relevant trials. Both pairwise meta-analyses and network meta-analyses were conducted to compare and rank the intervention strategies of whole grains and brans for the control of total cholesterol (TC), LDL cholesterol (LDL-C), HDL cholesterol (HDL-C), and triglycerides (TG). RESULTS: Fifty-five eligible trials with a total of 3900 participants were included. Cumulative ranking analyses showed that oat bran was the most effective intervention strategy for TC and LDL-C improvements, with significant decreases of - 0.35 mmol/L (95% CI - 0.47, - 0.23 mmol/L) and - 0.32 mmol/L (95% CI - 0.44, - 0.19 mmol/L) in TC and LDL-C compared with control, respectively. In comparison with control, oat was associated with significant reductions in TC by - 0.26 mmol/L (95% CI - 0.36, - 0.15 mmol/L) and LDL-C by - 0.17 mmol/L (95% CI - 0.28, - 0.07 mmol/L), which was ranked as the second best treatment. Barley, brown rice, wheat and wheat bran were shown to be ineffective in improving blood lipid compared with control. CONCLUSIONS: This network meta-analysis suggests that oat bran and oat are ranked higher than any other treatments for the regulations of TC and LDL-C, indicating that increasing oat sources of whole grain may be recommended for lipid control.
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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 | 0.021 | 0.041 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".