Serum Urate in Chronic Gout — Will It Be the First Validated Soluble Biomarker in Rheumatology?
Bibliographic record
Abstract
OBJECTIVE: To summarize evidence for and endorsement of serum urate (SU) as having fulfilled the OMERACT filter as a soluble biomarker in chronic gout at the 2010 Outcome Measures in Rheumatology Meeting (OMERACT 10). METHODS: Data were presented to support the use of SU as a soluble biomarker in chronic gout and specifically the ability to utilize it to predict future patient-reported outcomes. RESULTS: SU was accepted as having fulfilled the OMERACT filter by 78% of voters. However, consensus was not obtained regarding its use as a soluble biomarker in chronic gout. Although the majority of the criteria for a soluble biomarker were fulfilled, the key criterion of association of the biomarker with outcomes was not agreed upon. It was agreed that the appropriate choice of endpoint must be linked to its clinical importance to the individual with the disorder and its temporal relationship to the intervention. Appropriate outcomes in chronic gout may therefore include gout flares, reduction in tophi, and patient-reported outcomes. CONCLUSION: SU is a critical outcome measure. It has the potential to fulfil criteria for a soluble biomarker. Further analyses of existing data from randomized controlled trials will be required to determine whether SU can predict future important outcomes, in particular disability.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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; 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".