Vegetarian diets, low-meat diets and health: a review
Bibliographic record
Abstract
OBJECTIVE: To review the epidemiological evidence for vegetarian diets, low-meat dietary patterns and their association with health status in adults. DESIGN: Published literature review focusing primarily on prospective studies and meta-analyses examining the association between vegetarian diets and health outcomes. RESULTS: Both vegetarian diets and prudent diets allowing small amounts of red meat are associated with reduced risk of diseases, particularly CHD and type 2 diabetes. There is limited evidence of an association between vegetarian diets and cancer prevention. Evidence linking red meat intake, particularly processed meat, and increased risk of CHD, cancer and type 2 diabetes is convincing and provides indirect support for consumption of a plant-based diet. CONCLUSIONS: The health benefits of vegetarian diets are not unique. Prudent plant-based dietary patterns which also allow small intakes of red meat, fish and dairy products have demonstrated significant improvements in health status as well. At this time an optimal dietary intake for health status is unknown. Plant-based diets contain a host of food and nutrients known to have independent health benefits. While vegetarian diets have not shown any adverse effects on health, restrictive and monotonous vegetarian diets may result in nutrient deficiencies with deleterious effects on health. For this reason, appropriate advice is important to ensure a vegetarian diet is nutritionally adequate especially for vulnerable groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".