Bibliographic record
Abstract
The objectives of this article are to provide a brief overview of the scientific literature regarding the use of fenugreek in the management of hyperglycemia and dyslipidemia and suggest recommendations for additional research. Fenugreek (Trigonella foenum-graecum L., family Fabaceae) is an annual herb with triangular yellow flowers and seed-containing pods that grows in countries of the Mediterranean, Middle East, India, China, and, more recently, Canada. Fenugreek seed or its extracts are found in food products such as frozen dairy products, gelatin puddings, candy, and gravy sauces and in alcoholic and nonalcoholic beverages. An extract of fenugreek also is used as a flavoring ingredient in imitation maple syrup. Fenugreek has a history of use in traditional medicine in India and China. Its uses include as a treatment of weakness and leg edema, as a lactation and appetite stimulant, and as a remedy for indigestion, baldness, and fever. Some have used it topically for myalgia, wound treatment, and cellulitis. One potential benefit of fenugreek is improving elevated blood glucose and lipid levels associated with chronic conditions such as diabetes and obesity. Human investigations suggest that fenugreek can be beneficial as an adjunct in controlling high blood glucose and lipid levels in people with diabetes. However, larger, adequately powered, randomized, placebo-controlled, double-blind trials examining multiple measures of carbohydrate and lipid metabolism and insulin homeostasis are needed.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".