Reduction of cholesterol and fasting blood sugar levels by one month supplementation of fresh garlic in diabetic Libyan patients: A double blind, baseline controlled study
Bibliographic record
Abstract
Abstract Background: The therapeutic effects of fresh garlic remain controversial. The aim of this study is to investigate whether supplementation of fresh garlic could improve blood glucose and cholesterol profile in Libyan diabetic patients with moderate blood cholesterol. Methods: Forty-six diabetic patients were randomly assigned to either fresh garlic alone (≈2 grams/day), or fresh garlic in combination with glibenclamide taken on an empty stomach every morning for a month. Serum blood glucose, cholesterol and blood pressure were measured before starting treatment and after the end of the treatment period. Results: Fresh garlic alone was able to decrease the mean serum cholesterol levels by 26 mg/dl (84% of the original base values), while the combination of fresh garlic and glibenclamide produced a 28 mg/dl decrease in the mean serum cholesterol (85% of the original base values). Fresh garlic alone was able as well to decrease the mean blood glucose levels by 20 mg/dl (85% of the original base values), while the combination of fresh garlic and glibenclamide produced a 60 mg/dl decrease in the serum glucose levels (72% of the original base values). Neither treatment had a significant effect on the mean systolic or diastolic blood pressures after 30 days of treatment. Conclusion: Administration of fresh garlic every morning for a month significantly reduced the blood cholesterol and fasting blood glucose levels in diabetic patients. Thus administering dietary fresh garlic daily to diabetic patients might have cardio-protective effects on diabetic patients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".