Interventions for Tophi in Gout: A Cochrane Systematic Literature Review
Bibliographic record
Abstract
OBJECTIVE: To systematically review the available literature on the management of tophi in gout. This article is based on the Cochrane Review Interventions for Tophi in Gout published in the Cochrane Database of Systematic Reviews. METHODS: Medline, Embase, and The Cochrane Library were searched using a strategy developed with an experienced librarian. We also searched American College of Rheumatology and European League Against Rheumatism conference abstracts from 2010-2011. Included articles were reviewed in detail and a risk of bias (using the Cochrane tool) and quality assessment were performed. RESULTS: In total, 3206 references were recovered. Of these, 72 articles were selected based on our inclusion criteria. This included 1 report of 2 randomized controlled trials, 2 nonrandomized studies, and 69 case series and reports. The study with 2 randomized controlled trials looked at pegloticase. This showed improvement in tophi with treatment. One observational prospective trial looked at allopurinol and benzbromarone individually and in combination. It noted that achieving lower serum urate levels was associated with a faster reduction of tophi. An open-label extension trial noted that longterm maintenance of serum uric acid < 6.0 mg/dl with febuxostat led to a reduction in tophi. The case series and reports looked at surgical, pharmacological, and other interventions, as well as combination therapies. All surgical interventions reported improvement in pain and/or function. No report had objective measures of outcome. CONCLUSION: Treatment with urate-lowering therapy such as allopurinol, benzbromarone, allopurinol + benzbromarone in combination, febuxostat, or pegloticase can lead to reduction in tophi. There is some evidence that achieving a lower serum urate level leads to a faster rate of tophi reduction.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.021 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".