Bibliographic record
Abstract
PURPOSE OF REVIEW: The EAT-Lancet Commission on Healthy Diets from Sustainable Food Systems recommended a diet that is higher in plant-based food in 2019. Whilst plant-based diets have been followed in many regions of the world, the planetary concern and the published health benefits has increased the uptake of such diets. This review sets out to explore the impact on growth in children following a plant-based diet. RECENT FINDINGS: Twelve observational studies were published between 2017 and 2024, assessing the impact of vegan and vegetarian diets compared to omnivorous diets in children. Ten studies found no significant difference in growth parameters between children on the plant-based diet vs. the omnivorous diet, however there is a trend of lower growth parameters including undernutrition, but also lower rates of overweight in children on a plant-based diet. One study found that that children were shorter on a vegan diet and another found that infants born to mothers following a vegan diet had significantly lower weight. None of the studies assessed children that received professional dietary advice. SUMMARY: Overall, growth is comparable in children following a plant-based diet, but a higher risk for undernutrition has been highlighted. Children can follow a plant-based diet and establish good growth with the help of a qualified healthcare professional understanding macro and micronutrient provision on such a diet.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".