Preventing Attacks of Acute Gout When Introducing Urate-Lowering Therapy: A Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the evidence on treatment available to prevent an acute attack of gout when initiating a urate-lowering therapy (ULT) and for how long this treatment should be continued. To also evaluate the evidence on the optimal time to start a ULT after an acute attack of gout. METHODS: A systematic review as part of the 3e (Evidence, Expertise, Exchange) Initiative on Diagnosis and Management of Gout was performed using Medline, Embase, Cochrane Central Register of Controlled Trials (from 1950 to October 2011), and the European League Against Rheumatism (EULAR) and American College of Rheumatology (ACR) 2010/2011 meeting abstracts. Two reviewers independently screened titles and abstracts for selection criteria. Included articles were reviewed in detail, and a risk of bias assessment (using the Cochrane tool) was performed. RESULTS: The search identified 8168 articles and 197 abstracts, from which 4 randomized controlled trials were included in the review. Two of these studies compared placebo with colchicine, 1 compared differing durations of colchicine, and 1 compared colchicine with canakinumab. CONCLUSION: Two randomized controlled trials have shown that colchicine prophylaxis for at least 6 months, when starting a ULT, reduces the risk of acute attacks. Canakinumab, although not currently licensed for gout, has been shown to provide prophylaxis superior to colchicine, when starting a ULT. There is no evidence on the optimum time to start a ULT after an acute gout attack.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.008 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".