Epidemiology of gout in women: Fifty‐two–year followup of a prospective cohort
Bibliographic record
Abstract
OBJECTIVE: Despite the recent doubling of the incidence of gout among women and its substantial prevalence particularly in the aging female population, the risk factors for gout among women remain unknown. We undertook this study to evaluate purported risk factors for incident gout among women and to compare them with those among men. METHODS: Using prospective data from the Framingham Heart Study, we examined over a 52-year period (1950-2002) the relationship between purported risk factors and the incidence of gout in 2,476 women and 1,951 men. RESULTS: We documented 304 incident cases of gout, 104 of them among women. The incidence rates of gout for women per 1,000 person-years according to serum uric acid levels of <5.0, 5.0-5.9, 6.0-6.9, 7.0-7.9, and > or = 8.0 mg/dl were 0.8, 2.5, 4.2, 13.1, and 27.3, respectively (P for trend < 0.0001). The magnitude of this association was lower than that among men (P for interaction = 0.0002). Multivariate relative risks conferred by increasing age (per 5 years), obesity (body mass index > or = 30 kg/m(2)), alcohol intake (> or = 7 ounces of pure alcohol/week), hypertension, and diuretic use were 1.24, 2.74, 3.10, 1.82, and 2.39, respectively (all P < 0.05), for women. CONCLUSION: These prospective data with long-term followup provide evidence that higher levels of serum uric acid increase the risk of gout in a graded manner among women, but the rate of increase is lower than that among men. Increasing age, obesity, alcohol consumption, hypertension, and diuretic use were associated with the risk of incident gout among women.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".