Plant-based diet and risk of all-cause mortality: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective A systematic analysis was conducted to determine the relationship between a plant-based diet and all-cause mortality. Methods The PubMed, Embase and Web of Science databases were searched. Two authors selected English documents from the database. Then the other two authors extracted the data and evaluated the Newcastle–Ottawa Scale (NOS). This study adhered to the guidelines of the Preferred Reporting Project (PRISMA) and the PROSPERO Registry protocols. A mixed-effects model combined maximum adjusted estimates, with heterogeneity measured using the I2 statistic. The sensitivity analysis validated the analysis’s robustness, while publication bias was assessed. Results The results of the meta-analysis of 14 articles revealed that a plant-based diet (PDI) can reduce cancer mortality (RR = 0.88, [95% CI 0.79–0.98], τ2: 0.02, I2: 84.71%), cardiovascular disease (CVD) mortality (RR = 0.81, [95% CI 0.76–0.86], τ2: 0.00, I2: 49.25%) and mortality (RR = 0.84, [95% CI 0.79–0.89], τ2: 0.01, I2: 81.99%) risk. Adherence to a healthy plant-based diet (hPDI) was negatively correlated with cancer mortality (RR = 0.91, [95% CI 0.83–0.99], τ2:0.01, I2:85.61%), CVD mortality (RR = 0.85, [95% CI 0.77–0.94], τ2: 0.02, I2: 85.13%) and mortality (RR = 0.85, [95% CI 0.80–0.90], τ2: 0.01, I2: 89.83%). An unhealthy plant-based diet (uPDI) was positively correlated with CVD mortality (RR = 1.19, [95% CI 1.07–1.32], τ2: 0.02, I2: 80.03%) and mortality (RR = 1.18, [95% CI 1.09–1.27], τ2: 0.01, I2: 89.97%) and had a certain correlation with cancer mortality (RR = 1.10, [95% CI 0.97–1.26], τ2: 0.03, I2: 93.11%). Sensitivity analysis showed no contradictory results. Conclusion The hPDI was negatively associated with all-cause mortality, and the uPDI was positively associated with all-cause mortality. Systematic review registration https://www.crd.york.ac.uk/PROSPERO/#loginpage .
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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 | 0.012 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.035 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".