Tophus Measurement as an Outcome Measure for Clinical Trials of Chronic Gout: Progress and Research Priorities
Bibliographic record
Abstract
Despite the recognition that tophus regression is an important outcome measure in clinical trials of chronic gout, there is no agreed upon method of tophus measurement. A number of methods have been used in clinical trials of chronic gout, from simple physical measurement techniques to more complex advanced imaging methods. This article summarizes methods of tophus measurement and discusses their properties. Physical measurement using Vernier calipers meets most aspects of the Outcome Measures in Rheumatology (OMERACT) filter. Rigorous testing of the complex methods, particularly with respect to reliability and sensitivity to change, is needed to determine the appropriate use of these methods. Further information is also required regarding which method of physical measurement is best for use in future clinical trials. The need to develop and test a patient-reported outcome measure of tophus burden is also highlighted.
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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 | 0.397 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.004 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".