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 tocotrienols on the risk factors for atherosclerosis, plaque instability and thrombogenesis, and compares these effects with tocopherol. Tocotrienols reduce serum lipids and raise serum HDL-C. Alpha-tocopherol, on the other hand, has no effect on serum lipids. Tocotrienols have greater antioxidant activity than tocopherols. Both reduce the serum levels of C-reactive protein (CRP) and advanced glycation end products, and expression of cell adhesion molecules. The CRP-lowering effects of tocotrienols are greater than tocopherol. Tocotrienols reduce inflammatory mediators, δ-tocotrienol being more potent, followed by γ- and α-tocotrienol. Tocotrienols are antithrombotic and suppress the expression of matrix metalloproteinases. They suppress, regress and slow the progression of atherosclerosis, while tocopherol only suppresses, and has no effect on regression and slowing of progression of atherosclerosis. Tocotrienol reduces risk factors for destabilization of atherosclerotic plaques. There are no firm data to suggest that tocotrienols are effective in reducing the risk of cardiac events in established ischemic heart disease. Alpha-tocopherol is effective in primary prevention of coronary artery disease (CAD), but has no conclusive evidence that it has beneficial effects in patients with established ischemic heart disease. Tocotrienols are effective in reducing ischemia-reperfusion cardiac injury in experimental animals and has the potential to be used in patients undergoing angioplasty, stent implantation and aorto-coronary bypass surgery. In conclusion, experimental data suggest that tocotrienols have a potential for cardiovascular health, but long-term randomized clinical trials are needed to establish their efficacy in primary and secondary prevention of CAD.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| 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 | 0.001 |
| 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