Nuts and Nut-Based Products: A Meta-Analysis from Intake Health Benefits and Functional Characteristics from Recovered Constituents
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This review provides information on nutritional characteristics, extraction techniques, bioactive compounds, bioavailability and bioaccessibility through in vitro and in vivo assays on nuts and food products obtained from walnuts, such as almonds, walnuts, cashew nuts, pistachios, hazelnuts, walnuts, walnuts, macadamia nuts, Brazil nuts, pine nuts and peanuts. The influence of the consumption of these nuts on human health was carried out through a meta-analysis. Data meta-analysis indicated that nut consumption has a positive effect on total cholesterol, high-density lipoprotein, and low-density lipoprotein levels in the population. Although there are promising studies, more research is needed to determine the beneficial effects of these nuts when applied to products.Abbreviations: ALA: Alpha Linolenic Acid; Ca: Calcium; CVD: Cardiovascular Disease; CI: Confidence Interval; DBP: Diastolic Blood Pressure; EAE: Enzyme Assisted Extraction; GRAS: Generally Recognized as Safe; HDL: high-density lipoprotein; LDL: Low-Density Lipoprotein; Mg: Magnesium; MD: Mean Difference; MAE: Microwave Accelerated Extraction; MUFAS: Monounsaturated Fatty Acids; PUFAS: Polyunsaturated Fatty Acids; K: Potassium; PLE: Pressurized Liquid Extraction; SFAs: Saturated Fatty Acids; SD: Standard Deviation; SFE: Supercritical Fluid Extraction; SBP: Systolic Blood Pressure; UAE: Ultrasound Accelerated Extraction; Zn: Zinc
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it