Bibliographic record
Abstract
Gout is the most common inflammatory arthritis, with an increasing prevalence in many parts of the world1,2. Fewer studies have estimated the incidence of gout and its global trends. The Second and Third National Studies of Morbidity in General Practice in the United Kingdom estimated the respective gout incidence of 1.0 and 1.4 per 1000 person-years in the periods 1971–1975 and 1981–19823 while database-based research estimated an incidence of 1.19–1.80 per 1000 person-years in the period 1990–19994 and from 1.36–1.77 per 1000 person-years in the period 1997–20121. Data from the Rochester Epidemiology project estimated an age- and sex-adjusted incidence of gout of 45 per 100,000 people in the period 1977–1978 and 62 per 100,000 people in 1995–19965. In this issue of The Journal , Elfishawi, et al also used data from the Rochester Epidemiology project to compare the incidence of gout between periods 1989–1992 and 2009–20106. The incidence more than doubled, from 66.6 to 136.7 per 100,000 people. This study used the same cohort in Olmsted County, Minnesota, USA, and the same system of medical recording (for case identification) as the previous study by Arromdee, et al 5. They collectively provide serial measurements of gout incidence covering over 3 decades … Address correspondence to Dr. C.F. Kuo, Chang Gung Memorial Hospital, 5 Fu-Hsing St., Taoyuan, Taiwan 333; or Academic Rheumatology, Clinical Sciences Building, City Hospital, Nottingham, UK NG51PB. E-mail: zandis{at}gmail.com; zandis{at}adm.cgmh.org.tw
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 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".