Predictors of Mortality in People with Recent-onset Gout: A Prospective Observational Study
Bibliographic record
Abstract
Objective. To determine mortality rates and predictors of death at baseline in people with a recent onset of gout. Methods. People with gout disease duration < 10 years were recruited from primary and secondary care settings. Comprehensive clinical assessment was completed at baseline. Participants were prospectively followed for at least 1 year. Information about death was systematically collected from primary and secondary health records. Standardized mortality ratios (SMR) were calculated and risk factors for mortality were analyzed using Cox proportional hazard regression models. Results. The mean (SD) followup duration was 5.1 (1.6) years (a total 1511 patient-yrs accrued). Of the 295 participants, 43 (14.6%) had died at the time of censorship (SMR 1.96, 95% CI 1.44–2.62). In the reduced Cox proportional hazards model, these factors were independently associated with an increased risk of death from all causes: older age (70–80 yrs: HR 9.96, 95% CI 3.30–30.03; 80–91 yrs: HR 9.39, 95% CI 2.68–32.89), Māori or Pacific ethnicity (HR 2.48, 95% CI 1.17–5.29), loop diuretic use (HR 3.99, 95% CI 2.15–7.40), serum creatinine (per 10 µmol/l change; HR 1.04, 95% CI 1.00–1.07), and the presence of subcutaneous tophi (HR 2.85, 95% CI 1.49–5.44). The presence of subcutaneous tophi was the only baseline variable independently associated with both cardiovascular (CV) cause of death (HR 3.13, 95% CI 1.38–7.10) and non-CV cause of death (HR 3.48, 95% CI 1.25–9.63). Conclusion. People with gout disease duration < 10 years have an increased risk of death. The presence of subcutaneous tophi at baseline is an independent predictor of mortality, from both CV and non-CV causes.
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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) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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".