Traditional Chinese Medicine, Food Therapy, and Hypertension Control: A Narrative Review of Chinese Literature
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the lack of English literature about Traditional Chinese Medicine (TCM) food therapy, there is abundant Chinese literature about the application of food therapy for hypertension control. This paper summarizes basic concepts of TCM, the principles of food therapy and its application for hypertension control according to Chinese literature. In TCM, food is conceptualized according to both nutritional and functional aspects, and can be used to treat illnesses. Four principles of TCM food therapy including light eating, balancing the "hot" and "cold" nature of food, the harmony of the five flavors of food, and consistency between dietary intake and different health conditions, can be used to facilitate hypertension control. Based on a statistical analysis of antihypertensive foods recommended in 20 books on the application of food therapy for hypertension control, the 38 most frequently recommended are celery, tomato, banana, hawthorn, garlic, onion, seaweed, apple, corn, green beans, persimmon, laver, kiwi, watermelon, eggplant, carrots, mushroom, peanut, soy products, sea cucumber, buckwheat, garland chrysanthemum, spinach, honey, dairy products, vinegar, black fungus, jellyfish, green onion, shepherd's purse, soybean, potato, pear, winter melon, bitter melon, oat, pea, and tea. Food therapy emphasizes the therapeutic effects of food, considering its nature, taste, and function on human balanced health, which leads to optimal blood pressure control. Current literature suggests that food therapy is effective in blood pressure control and can be incorporated into blood pressure self-management in the Chinese population.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it