Unclosing Premature Mortality Gap Among Patients With Gout in the <scp>US</scp> General Population, Independent of Serum Urate and Atherosclerotic Cardiovascular Risk Factors
Bibliographic record
Abstract
OBJECTIVE: Gout flares are followed by transient major cardiovascular (CV) risk, implicating the role of inflammation; the aim of this study was to determine whether premature mortality rates in patients with gout and CV risk are independent of serum urate (SU) and atherosclerotic CV disease (ASCVD) risk factors. METHODS: Using serial US nationwide prospective cohorts, we evaluated the independent association of prevalent gout with all-cause and CV mortality, adjusting for SU, ASCVD risk factors, comorbidities, medications, and kidney function and compared mortality rates between the early (1988-1994 baseline) and late cohorts (2007-2016 baseline). We replicated late cohort findings among patients with gout in a nationwide UK cohort (2006-2010 baseline). RESULTS: Adjusted hazard ratios (HRs) for mortality rates in patients with prevalent gout were similar in early and late US cohorts (1.20 [1.03-1.40] and 1.19 [1.04-1.37], respectively); HRs with further adjustment for SU were 1.19 (1.02-1.38) and 1.19 (1.03-1.37), respectively. Adjusted HR among patients with gout from the UK late cohort was 1.61 (1.47-1.75); these associations were larger among women (P = 0.04) and prominent among Black individuals. Adjusted HR for CV mortality rates in the late US cohort was 1.39 (1.09-1.78); those for circulatory, CV, and coronary heart disease deaths among UK patients with incident gout were 1.48 (1.24-1.76), 1.49 (1.20-1.85), and 1.59 (1.26-1.99), respectively. CONCLUSIONS: Patients with gout experience a persistent mortality gap in all-cause and CV deaths, even adjusting for SU and ASCVD risk factors, supporting a role for gout-specific pathways (eg, flare inflammation). These findings suggest gaps in current care, particularly in women and possibly among Black patients.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".