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
This review emphasizes the effects of resveratrol on factors involved in the mechanism of atherosclerosis and risk factors for atherosclerosis. The effects of wine and resveratrol on atherosclerosis are also discussed. Resveratrol is a potent antioxidant and an anti-inflammatory agent. It reduces the expression of cell adhesion molecules, monocyte colony stimulating factors, matrix metalloproteinases, and growth factors; and inhibits platelet aggregation and vascular smooth muscle cell proliferation. It reduces the serum levels of total cholesterol, triglycerides (TG), and raises high-density lipoprotein cholesterol, inhibits expression of C-reactive protein and lowers the levels of advanced glycation end products and its receptor in the vascular tissue. It lowers the risk factors for plaque rupture. Epidemiological data show that moderate consumption of alcohol has an inverse association with carotid atherosclerosis while high consumption has a positive association with carotid atherosclerosis. Wine reduces the extent of atherosclerosis in animal model. The antiatherosclerotic effect of wine is mainly due to it resveratrol content. Resveratrol reduces the extent of atherosclerosis in animal model of atherosclerosis (apolipoprotein [Apo] E-deficient and Apo E(-/-)/low-density lipoprotein receptor-deficient mice and macrophage). In rabbit model of atherosclerosis, both reduction and acceleration of atherosclerosis have been reported with resveratrol. There are no data for regression and slowing of progression of atherosclerosis. Robust clinical trials for suppression of atherosclerosis are lacking. In conclusion, resveratrol has potential but experimental studies in depth and robust clinical trials are lacking for this agent to be of any value in the primary and secondary prevention of coronary and peripheral artery disease.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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