Is eating a mixed diet better for health and survival?: A systematic review and meta-analysis of longitudinal observational studies
Bibliographic record
Abstract
The role of dietary diversity in chronic disease or survival is controversial. This meta-analysis quantified the health impact of dietary diversity. Random-effects models pooled risk ratios (RRs) and 95% confidence intervals (CIs) of 20 longitudinal studies. Total dietary diversity was associated with a 22% lower risk of all-cause mortality (RR 0.78 [95%CI: 0.64, 0.96]), and was inversely associated with incident cancer- or CVD-specific mortality only in subgroup analyses (RR range: 0.53 to 0.90, p < 0.05). Similarly, diversity across healthy foods was inversely associated with all-cause mortality (RR 0.84 [95%CI: 0.73, 0.96]). An inverse association between total diet diversity and incident CVD was significant in non-European populations consuming diets with diverse food groups (RR: 0.93 [95% CI: 0.86-0.99]). Effects on cancer risk are unstudied. Diversity within fruits and/or vegetables showed null associations for all outcomes, except potentially for squamous cell-type carcinomas. More robust research is warranted. Findings indicated greater dietary diversity may benefit overall survival.
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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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.033 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".