Gout and the Risk of Incident Erectile Dysfunction: A Body Mass Index-matched Population-based Study
Bibliographic record
Abstract
OBJECTIVE: Gout is the most common inflammatory arthritis. Erectile dysfunction (ED) is common in the general population; however, evidence regarding ED among patients with gout is limited. Our purpose was to study the association between incident gout and the risk of incident ED in the general population. METHODS: We conducted a cohort study using The Health Improvement Network, an electronic medical record database in the United Kingdom. Up to 5 individuals without gout were matched to each case of incident gout by age, enrollment time, and body mass index (BMI). Multivariate HR for ED were calculated after adjusting for smoking, alcohol consumption, comorbidities, and medication use. RESULTS: We identified 2290 new cases of ED among 38,438 patients with gout (mean age 63.6 yrs) and 8447 cases among 154,332 individuals in the comparison cohort over a 5-year median followup (11.9 vs 10.5 per 1000 person-years, respectively). Univariate (matched for age, entry time, and BMI) and multivariate HR for ED among patients with gout were 1.13 (95% CI 1.08-1.19) and 1.15 (95% CI 1.09-1.21), respectively. In our sensitivity analysis, by restricting gout cases to those receiving anti-gout treatment (n = 27,718), the magnitude of relative risk was stronger than the primary analysis (multivariate HR 1.31, 95% CI 1.23-1.39). CONCLUSION: This population-based study suggests that gout is associated with an increased risk of developing ED, supporting a possible role for hyperuricemia and inflammation as independent risk factors for ED.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".