Variability in the Reporting of Serum Urate and Flares in Gout Clinical Trials: Need for Minimum Reporting Requirements
Bibliographic record
Abstract
OBJECTIVE: To describe the ways in which serum urate (SU) and gout flares are reported in clinical trials, and to propose minimum reporting requirements. METHODS: This analysis was done as part of a systematic review aiming to validate SU as a biomarker for gout. The ways in which SU and flares were reported were extracted from each study by 2 reviewers. RESULTS: A total of 22 studies (10 randomized controlled trials, 3 open-label extension studies, and 9 observational studies) were identified. There were 3 broad categories of SU reporting: percentage at target SU, mean SU, and change in SU. A median of 2 (range 1-3) categories were reported across all studies. The most common method of reporting SU was percentage at target in 17/22 (77.3%) studies, with all studies reporting a target of SU < 6 mg/dl. There were 12/22 (54.5%) studies reporting mean SU at some time after study entry, with 7 (58.3%) of these reporting at more than just the final study visit. Two ways of reporting gout flares were identified: mean flare rate and percentage of participants with flares. There was variability in time periods over which flares rates were reported. CONCLUSION: There is inconsistent reporting of SU and flares in gout studies. Reporting the percentage of participants who achieve a target SU reflects international treatment guidelines. SU should also be reported as a continuous variable with a relevant central and dispersion estimate. Gout flares should be reported as both percentage of participants and mean flare rates at each timepoint.
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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 | 0.838 | 0.934 |
| Meta-epidemiology (narrow) | 0.004 | 0.006 |
| Meta-epidemiology (broad) | 0.017 | 0.024 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.016 | 0.016 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.011 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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; the direct Gemma label and the distilled Codex classifier 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".