Are novel plant-based meat alternatives the healthier choice?
Bibliographic record
Abstract
The global market for plant-based meat alternatives (PBMAs) is expanding quickly. In this narrative review, analysis of the most recent scientific literature was achieved to understand the nutritional profile, health implications, and the challenges faced by PBMAs. On the positive side, most PBMAs are good sources of dietary fiber, contain phytochemicals, have comparable levels of iron, and are lower in calories, saturated fat, and cholesterol than meat. However, PBMAs frequently contain anti-nutrients, have less protein, iron, and vitamin B12, are lower in protein quality, and also have higher amounts of sodium. Substituting PBMAs for meats may cause iron, vitamin B12, and less likely protein deficiency for these vulnerable population such as women, older adults, and individuals with disorders. PBMAs fall into the category of ultra-processed foods, indicating a need to develop minimally processed, clean-label products. Replacing red meat with healthy plant-based foods is associated with lower risks of cardiovascular diseases, type 2 diabetes, and total mortality. There is a lack of robust, long-term evidence on the role of PBMAs consumption in health. As the nutrient contents of PBMAs can vary, consumers must read nutrition facts labels and ingredient lists to select a product that best fits their nutritional and health objectives.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".