Bibliographic record
Abstract
BACKGROUND: Hyperuricemia is associated with reduced survival among patients with heart failure (HF), but the effect of gout on HF outcomes is unknown. A recent randomized trial suggested that allopurinol may reduce adverse outcomes among patients with hyperuricemia and HF. Our objective was to determine whether gout and allopurinol use are associated with HF outcomes. METHODS: Time-matched, nested case-control analysis of a retrospective cohort of patients with HF who were 66 years or older using health care databases in Quebec, Canada. The primary outcome measure was a composite measure of HF readmission and all-cause mortality. The secondary outcome measure was all-cause mortality. Rate ratios were calculated using conditional logistic regression and adjusted for known prognostic factors. RESULTS: Of the 25,090 patients in this cohort, 14,327 experienced the primary outcome. Both a remote history of gout and an acute episode of gout (within 60 days of the event date) were associated with an increased risk of HF readmission or death (adjusted rate ratio, 1.63; 95% confidence interval, 1.48-1.80; P<.001 and 2.06; 1.39-3.06; P<.001, respectively). Continuous allopurinol use (>30 days of continuous use) was not associated with the primary outcome among the overall population with HF (adjusted rate ratio, 1.02; 95% confidence interval, 0.95-1.10; P=.55) but was associated with reduced HF readmissions or death (0.69; 0.60-0.79; P<.001) and all-cause mortality (0.74; 0.61-0.90; P<.001) among patients with a history of gout. CONCLUSIONS: Patients with HF and a history of gout represent a high-risk population. Among such patients, the use of allopurinol is associated with improved outcomes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".