A systematic review and meta-analysis of nut consumption and incident risk of CVD and all-cause mortality
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
Dietary patterns containing nuts are associated with a lower risk of CVD mortality, and increased nut consumption has been shown to have beneficial effects on CVD risk factors including serum lipid levels. Recent studies have reported on the relationship between nut intake and CVD outcomes and mortality. Our objective was to systematically review the literature and quantify associations between nut consumption and CVD outcomes and all-cause mortality. Five electronic databases (through July 2015), previous reviews and bibliographies of qualifying articles were searched. In the twenty included prospective cohort studies (n 467 389), nut consumption was significantly associated with a lower risk of all-cause mortality (ten studies; risk ratio (RR) 0·81; 95 % CI 0·77, 0·85 for highest v. lowest quantile of intake, P het=0·04, I 2=43 %), CVD mortality (five studies; RR 0·73; 95 % CI 0·68, 0·78; P het=0·31, I 2=16 %), all CHD (three studies; RR 0·66; 95 % CI 0·48, 0·91; P het=0·0002, I 2=88 %) and CHD mortality (seven studies; RR 0·70; 95 % CI 0·64, 0·76; P het=0·65, I 2=0 %), as well as a statistically non-significant reduction in the risk of non-fatal CHD (three studies; RR 0·71; 95 % CI 0·49, 1·03; P het=0·03, I 2=72 %) and stroke mortality (three studies; RR 0·83; 95 % CI 0·69, 1·00; P het=0·54, I 2=0 %). No evidence of association was found for total stroke (two studies; RR 1·05; 95 % CI 0·69, 1·61; P het=0·04, I 2=77 %). Data on total CVD and sudden cardiac death were available from one cohort study, and they were significantly inversely associated with nut consumption. In conclusion, we found that higher nut consumption is associated with a lower risk of all-cause mortality, total CVD, CVD mortality, total CHD, CHD mortality and sudden cardiac death.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| 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) | 0.000 | 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 it