Dietary carbohydrate quality index and cardio-metabolic risk factors
Bibliographic record
Abstract
Abstract: Introduction: Improving the quality of diet is known as one of the practical ways to reduce cardio-metabolic risk factors (CMRFs). The carbohydrate quality index (CQI) is a relatively new index to evaluate diet quality. It is calculated based on the ratio of solid carbohydrates to total carbohydrates, dietary fibre intake, glycemic index and the ratio of whole grains to total grains. This systematic review and meta-analysis was designed to investigate the association between dietary CQI and CMRFs. Methods: In this systematic review, some international databases, including Scopus, PubMed, EMBASE, Web of Science, and Google Scholar up to July 2022, were searched according to appropriate keywords. All observational studies with an English full text assessing the association between the dietary CQI and CMRFs were included. Two researchers independently extracted the data and assessed the quality of the articles with the Newcastle-Ottawa Scale. Random/fixed-effect meta-analysis was used to pool standardized mean difference (SMD) as an effect size. Results: 11 studies with a total of 63962 subjects were found to be eligible and included in the qualitative synthesis; only BMI, WC and metabolic syndrome reached the threshold of 3 reports with the same effect size and thus only 5 were included in the meta-analysis. The main finding of the included studies was that there were inverse associations between CQI and CMRFs, mainly obesity, glucose metabolism indices, and blood pressure. In the five studies included in the random effect meta-analysis, the association between CQI and body mass index (SMD: 0.45, 95%CI: −0.12, 1.01), waist circumference (SMD: −0.09, 95%CI: −0.34, 0.15) and metabolic syndrome (SMD: 0.63, 95%CI: −0.01, 1.28) was not statistically significant. Conclusion: Although the qualitative findings support the positive association of CQI with CMRFs, the evidence is insufficient to conclude robust findings. Further observational and interventional studies are needed to clearly elucidate this association.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.000 | 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".