Inulin-type fructans supplementation and cardiovascular disease risk factors
Bibliographic record
Abstract
Cardiovascular diseases (CVDs) are a leading cause of mortality worldwide, with obesity being a major contributor due to unhealthy dietary habits and sedentary lifestyles. Poor dietary habits increase the risk of obesity, diabetes, hypertension, and other CVDs. However, a new concept called functional food has emerged as a potential solution to this problem. Functional foods are those that provide health benefits beyond basic nutrition and can reduce the risk of diseases. Prebiotics, like inulin-type fructans (ITF), are considered functional foods. These ITFs have been extensively studied and are the only prebiotics that have generated sufficient evidence to enable a comprehensive assessment of their potential as functional food components. They are commonly used in various food products, such as biscuits, bread, cereals, confectionery, drinks, infant feeds, sauces, table spreads, and yogurts, to improve organoleptic quality and a better-balanced nutritional composition. However, the available evidence provides conflicting results regarding the beneficial effects of ITF on health. Given the increased use of ITF in the food industry, we conducted a systematic review and meta-analysis (SRMA) to assess their effects on CVD risk factors. In this thesis, we first describe the methods used in the SRMA, which were published in a peer-reviewed journal. Subsequently, we present the results of the SRMA. The next two chapters discuss the reporting quality of randomized trials and abstracts of randomized controlled trials included in our SRMA. Finally, we summarize the methodological contributions of this thesis. Through our work, we hope to contribute to the growing body of evidence regarding the use of functional foods like ITF as a means of reducing the risk of CVDs and promoting healthier dietary habits.
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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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".