Evidences of the cardioprotective potential of fruits: The case of cranberries
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
Eating a healthy balanced diet, is one of the most important and relevant ways to delay and prevent various health complications including cardiovascular disease (CVD). Among the nutritional factors that have been investigated in recent years, dietary fat intake may be the one that has been most targeted. However, there is also clear epidemiological evidence that increased fruits and vegetables intake can significantly reduce the risk of CVD, an effect that has been suggested to be resulting to a significant extent, from the high polyphenol content of these foods. Numerous polyphenolic compounds such as flavonoids have been identified as having strong antioxidant properties. Most interesting is the fact that, in addition to being one of the largest groups of antioxidant phytochemicals, flavonoids are also an integral part of the human diet as they are found in most fruits and vegetables. Cranberries are one of the most important sources of flavonoids that have a strong antioxidant and anti-inflammatory capacities. Thus, consumption of cranberries or their related products could be of importance not only in the maintenance of health but also in preventing CVD. The following review will present evidences supported for the most part by clinical observations that cranberries can exert potentially healthy effects for your heart.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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