Bibliographic record
Abstract
PURPOSE OF REVIEW: This review summarizes recent epidemiologic research findings on gout, and attempts to put them into the context of clinical and public health decision-making aimed at prevention and improved management of gout. RECENT FINDINGS: A large prospective study found that coffee consumption was inversely associated with risk of gout and that consumption of sugar-sweetened soft drinks or fructose was strongly associated with an increased gout risk. Studies based on the Third National Health and Nutrition Examination Survey (NHANES III) suggest that these consumptions affect serum uric acid levels parallel to the direction of gout risk. Furthermore, data from NHANES III show a remarkably high prevalence of the metabolic syndrome among individuals with gout. Prospective studies found an increased risk of myocardial infarction and cardiovascular mortality in gout patients. SUMMARY: Lifestyle and dietary recommendations for gout patients should consider other health benefits, since gout is often associated with major chronic disorders such as the metabolic syndrome and an increased risk for cardiovascular disease and mortality. In addition to recent dietary recommendations, gout patients should be advised to limit fructose intake. The inverse link between coffee and the risk of gout suggests that coffee could be allowed among gout patients.
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) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.008 | 0.003 |
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".