Epidemiology, risk factors, and lifestyle modifications for gout.
Bibliographic record
Abstract
Gout affects more than 1% of adults in the USA, and it is the most common form of inflammatory arthritis among men. Accumulating data support an increase in the prevalence of gout that is potentially attributable to recent shifts in diet and lifestyle, improved medical care, and increased longevity. There are both nonmodifiable and modifiable risk factors for hyperuricemia and gout. Nonmodifiable risk factors include age and sex. Gout prevalence increases in direct association with age; the increased longevity of populations in industrialized nations may contribute to a higher prevalence of gout through the disorder's association with aging-related diseases such as metabolic syndrome and hypertension, and treatments for these diseases such as thiazide diuretics for hypertension. Although gout is considered to be primarily a male disease, there is a more equal sex distribution among elderly patients. Modifiable risk factors for gout include obesity, the use of certain medications, high purine intake, and consumption of purine-rich alcoholic beverages. The increasing prevalence of gout worldwide indicates that there is an urgent need for improved efforts to identify patients with hyperuricemia early in the disease process, before the clinical manifestations of gout become apparent.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".