Cannabis‐infused foods: Phytonutrients, health, and safe product innovations
Bibliographic record
Abstract
Cannabis-infused foods are currently on the rise in markets all around the world. Meanwhile, there are concerns over the health implications for consumers. Studies have explored the therapeutic potential and nutritional and economic benefits of cannabis usage. Yet, the phytonutrients, processing methods, and health implications of cannabis-infused foods have not been well explored. This review evaluates existing evidence on the nutritional, processing, safety, and phytonutrient composition of cannabis-infused food products and their medicinal and functional prospects. Cannabis seeds contain the highest amount of dietary nutrients, while flowers contain the highest amount of bioactive constituents. Oils, butter, seeds, flowers, and leaf extracts are the plant forms currently incorporated into food products such as beverages, baked products, cooking ingredients, functional foods, nutraceuticals, and nootropics. Cannabis-infused foods have been found to offer therapeutic benefits for pain management, brain function, gut health, and certain cancers. Findings also show significant constraints associated with cannabis-infused foods regarding dosage guidelines, limited research, efficacy, and long-term health effects on consumers. This is further worsened by the lack of policies that regulate the industry. To realize the full potential of cannabis use in the food and health industries and in research, regulatory guidelines are needed to control dosages and improve its efficient use in these industries. This will go a long way to ensure the safety of cannabis users and enhance responsible production, marketing, and distribution.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".