Corticosteroid or Nonsteroidal Antiinflammatory Drugs for the Treatment of Acute Gout: A Systematic Review of Randomized Controlled Trials
Bibliographic record
Abstract
OBJECTIVE: Nonsteroidal antiinflammatory drugs (NSAID) are used as first-line agents to treat acute gout. Recent trials suggest a possible first-line role for corticosteroids. METHODS: We conducted a metaanalysis of randomized controlled trials (RCT) evaluating corticosteroid versus NSAID therapy (nonselective and selective) as treatment for acute gout. MEDLINE, EMBASE, and CENTRAL were systematically searched through August 2016. Outcomes included pain, bleeding, joint swelling, erythema, tenderness, activity limitation, response to therapy, quality of life, time to resolution, supplementary analgesics, and adverse events. Evidence quality was summarized using the GRADE (Grading of Recommendations Assessment, Development, and Evaluation) system. RESULTS: Six eligible trials (817 patients) were identified. The mean study followup was 15 days (range 4-30). Risks of bias were generally low. In low- to moderate-quality evidence, corticosteroids did not have different effects on pain score at < 7 days [standardized mean difference (SMD) -0.09, 95% CI -0.26 to 0.08] or at ≥ 7 days (SMD 0.32, 95% CI -0.27 to 0.92) when compared with NSAID. There was no evidence of different risks of gastrointestinal bleeding [relative risk (RR) 0.09, 95% CI 0.01-1.67]. There was no evidence of different responses to therapy on pain at < 7 days (RR 1.07, 95% CI 0.80-1.44) and ≥ 7 days, time to disease resolution, or number of supplementary analgesics used (MD 2.10 drugs, 95% CI -1.01 to 5.21). There was a lower risk of indigestion (RR 0.50, 95% CI 0.27-0.92), nausea (RR 0.25, 95% CI 0.11-0.54), and vomiting (RR 0.11, 95% CI 0.02-0.56) with corticosteroid therapy. CONCLUSION: There is no evidence that corticosteroids and NSAID have different efficacy in managing pain in acute gout, but corticosteroids appear to have a more favorable safety profile for selected adverse events analyzed in existing RCT.
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 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 | 0.028 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.062 | 0.010 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; both teacher heads agree on what is shown here.
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".