Mortality in Patients With Gout Treated With Allopurinol: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: Urate-lowering therapy (predominantly allopurinol) is highly effective as a treatment for gout, but its wider long-term effects remain unclear. This systematic review and meta-analysis aimed to ascertain the association between mortality and the use of allopurinol in patients with gout. METHOD: Medline, Embase, CINAHL, and the Cochrane Library were searched from inception to August 2018. Articles eligible for inclusion used a cohort design and examined cardiovascular or all-cause mortality in patients diagnosed with gout and prescribed allopurinol. Information on study characteristics, design, sample size, and mortality risk estimates were extracted. Article quality was assessed using the Newcastle-Ottawa Scale. Included articles were described in a narrative synthesis and, where possible, risk estimate data were pooled. RESULTS: Four articles reported a hazard ratio (HR) risk estimate for all-cause mortality in patients with gout using allopurinol, and 2 of these also reported cardiovascular mortality. Two articles found allopurinol to be protective in patients with gout, 1 found no statistically significant association, and 1 found no statistically significant effect of escalation of allopurinol dosage on all-cause or cardiovascular-related mortality. Data pooling was possible for all-cause mortality and found no association between allopurinol use in patients with gout and all-cause mortality compared to patients with gout not using allopurinol (adjusted HR 0.80 [95% confidence interval 0.60-1.05]). CONCLUSION: There was no significant association between all-cause mortality and allopurinol use in people with gout. However, the number of included studies was small, suggesting that further studies are needed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.011 | 0.001 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".