Efficacy and Safety of Pharmacologic Interventions in Patients Experiencing a Gout Flare: A Systematic Review and Network Meta‐Analysis
Bibliographic record
Abstract
OBJECTIVE: To compare the relative efficacy and safety of pharmacologic antiinflammatory interventions for gout flares. METHODS: We searched Ovid Medline, Embase, and Cochrane library for randomized controlled trials (RCTs) that compared pharmacologic antiinflammatory treatment of gout flares. We conducted a network meta-analysis (NMA) using a frequentist framework and assessed the certainty of evidence and made conclusions using the Grading of Recommendations Assessment, Development, and Evaluation for NMA. RESULTS: In the 30 eligible RCTs, canakinumab provided the highest pain reduction at day 2 and at longest follow-up (mean difference relative to acetic acid derivative nonsteroidal antiinflammatory drugs [NSAIDs] -41.12 [95% confidence interval (95% CI) -53.36, -29.11] on a 0-100 scale at day 2, and mean difference -12.84 [95% CI -20.76, -4.91] at longest follow-up; both moderate certainty; minimum important difference -19). Intravenous or intramuscular corticosteroids were inferior to canakinumab but may be better than the other commonly used interventions (low to very low certainty). For joint tenderness, canakinumab may be the most effective intervention at day 2. Acetic acid derivative NSAIDs improved joint swelling better than ibuprofen NSAIDs at day 2 (mean difference -0.29 [95% CI -0.56, -0.02] on a 0-4 scale; moderate certainty) and improved patient global assessment (PtGA) greater than ibuprofen NSAIDs at the longest follow-up (mean difference -0.44 [95% CI -0.86, -0.02]; moderate). CONCLUSION: Canakinumab may be superior to other alternatives and intravenous or intramuscular corticosteroids may be the second best in pain reduction. Acetic acid derivative NSAIDs may be superior to ibuprofen NSAIDs in improving joint swelling and PtGA.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".