The Rising Incidence of Gout and the Increasing Burden of Comorbidities: A Population-based Study over 20 Years
Bibliographic record
Abstract
Objective. To examine the incidence of gout over the last 20 years and to evaluate possible changes in associated comorbid conditions. Methods. The medical records were reviewed of all adults with a diagnosis of incident gout in Olmsted County, Minnesota, USA, during 2 time periods (January 1, 1989–December 31, 1992, and January 1, 2009–December 31, 2010). Incident cases had to fulfill at least 1 of 3 criteria: the American Rheumatism Association 1977 preliminary criteria for gout, the Rome criteria, or the New York criteria. Results. A total of 158 patients with new-onset gout were identified during 1989–1992 and 271 patients during 2009–2010, yielding age- and sex-adjusted incidence rates of 66.6/100,000 (95% CI 55.9–77.4) in 1989–1992 and 136.7/100,000 (95% CI 120.4–153.1) in 2009–2010. The incidence rate ratio was 2.62 (95% CI 1.80–3.83). At the time of their first gout flare, patients diagnosed with gout in 2009–2010 had higher prevalence of comorbid conditions compared with 1989–1992, including hypertension (69% vs 54%), diabetes mellitus (25% vs 6%), renal disease (28% vs 11%), hyperlipidemia (61% vs 21%), and morbid obesity (body mass index ≥ 35 kg/m2; 29% vs 10%). Conclusion. The incidence of gout has more than doubled over the recent 20 years. This increase together with the more frequent occurrence of comorbid conditions and cardiovascular risk factors represents a significant public health challenge.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".