Bibliographic record
Abstract
Gout is a common inflammatory arthritis triggered by the crystallization of uric acid within the joints1. Gout causes severe pain and suffering and is a substantial cause of morbidity. Further, emerging evidence suggests that gout is strongly associated with the metabolic syndrome2 and may lead to myocardial infarction3–5, diabetes6, and premature death7,8. A number of epidemiological studies from a diverse range of countries suggest that gout has increased in prevalence and incidence in the past few decades. Using the NHANES III age/sex prevalence and the corresponding 2005 population estimates from the US Census Bureau, it is estimated that up to 6.1 million adults aged ≥ 20 years have ever had gout9. Consequently, gout has a significant economic impact in society due to both direct medical costs and indirect costs9–12. A substantial proportion of gout patients under the care of physicians fail to achieve adequate control of hyperuricemia or symptoms13. Recent studies indicate that the majority of gout patients under the care of physicians are not adequately managed with currently available anti-gout therapies13–17. These gout cases have been referred to as “treatment-failure gout” and have become the primary target for quality improvement of care, including new drug development13,18–23. Although recent treatment guidelines and increased educational efforts could improve the quality of gout care, even under the very best of conditions, between 100,000 and 300,000 in … Address reprint request to Dr. Choi. E-mail: dr.choi{at}yahoo.com Dr. Kim is supported by NIH T32 (AR07442) Training Program in Rheumatic Disease; Dr. Choi served on the advisory board for Takeda and Savient Pharmaceuticals.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.008 |
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".