From the kitchen to the medicine cabinet: Examples of functional herbs and spices
Bibliographic record
Abstract
In many cultures, "food as medicine" has existed for centuries as the foundation of health. It is a practice built on the knowledge that food and diet play important roles in disease prevention and management. Foods possessing therapeutic properties are often referred to as functional foods. Many herbs and spices contain numerous nutritional and non-nutritional components that can interact with pharmacologically relevant receptors, either directly or indirectly via their metabolites, to regulate cellular biochemical processes. Although opinions are changing, the concept of food as a therapeutic intervention goes against conventional Western medicine. To provide guidance to clinicians interested in using these products, members of the Food as Medicine working group of the Nutrition Committee NASPGHAN, as part of a two-part review series, have identified frequently used foods, supplements, herbs, and spices that are utilized for therapeutic intent and have created summaries of commonly used indications, doses, and caveats. In this review, the focus is the use of select herbs and spices for medicinal purposes. Gaps in our knowledge in how to effectively use these agents in pediatric patients are discussed. Evidence supporting their use for management of gastrointestinal conditions, especially in the pediatric population, is provided when available. Circumstances in clinical settings and patient indications may require actions different from those recommended in this review and professional judgment should prevail.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 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) | 0.006 | 0.002 |
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".