Bibliographic record
Abstract
Arthritis is a global health concern affecting a significant proportion of the population and associated with reduced quality of life. Among the different forms of arthritis, osteoarthritis (OA) and rheumatoid arthritis (RA) are the most common and lacking a definite cure in the affected individuals. Fruits, such as berries and pomegranates are rich sources of a variety of dietary bioactive compounds, especially the polyphenolic flavonoids that have been associated with antioxidant, anti-inflammatory and analgesic effects. Emerging research demonstrates a protective role of fruits and their polyphenols in pre-clinical, clinical and epidemiological studies of OA and RA. In this context, commonly available fruits, such as blueberries, raspberries and strawberries, and pomegranates have shown promising results in reducing pain and inflammation in experimental models and in human clinical studies of arthritis. There is also some evidence on the role of specific fruit polyphenols, such as quercetin and citrus flavonoids in alleviating RA symptoms. These emerging data deserve further investigation in rigorous scientific studies to determine the mechanisms, dosing and selection of fruits and fruit extracts in arthritis management.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".