Exploring avocado consumption and health: a scoping review and evidence map
Bibliographic record
Abstract
Objective: This scoping review evaluates the breadth of research on avocado intake and health, considering all populations and health outcomes (registered on Open Science Foundation at https://osf.io/nq5hk). Design: Any human intervention or observational study where effects could be isolated to consumption of avocado were included. A systematic literature search through April 2024 was conducted (PubMed, Web of Science, Scopus, and CENTRAL) and supplemented by backwards citation screening. Dual screening, data extraction, and conflict resolution were performed by three reviewers and an interactive evidence map was created. Results: After deduplication, 8,823 unique records were retrieved; 58 articles met inclusion criteria, comprising 45 unique studies (28 interventions, 17 observational studies). Studies were largely conducted in the United States or Latin America and generally included adults, with overweight/obesity, frequently with elevated lipid concentrations. Interventions assessed the impact of diets enriched in monounsaturated fatty acids, diets higher/lower in carbohydrates, or in free-feeding conditions. Larger amounts of avocados were used in interventions than commonly consumed in observational studies (60-300 vs. 0-10 g/d, respectively). Blood lipids, nutrient bioavailability, cardiovascular risk, glycemia, and anthropometric variables were the most common outcomes reported across all studies. Conclusion: Future recommendations for novel research include the study of: European, Asian, adolescent or younger, and senior populations; dose-response designs and longer length interventions; dietary compensation; and the need for greater replication. The results have been made public and freely available, and a visual, interactive map was created to aid in science translation. This evidence map should enable future meta-analyses, enhance communication and transparency in avocado research, and serve as a resource for policy guidance.
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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 | 0.027 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.039 | 0.033 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".