Patient-reported Outcomes in Chronic Gout: A Report from OMERACT 10
Bibliographic record
Abstract
OBJECTIVE: To summarize the endorsement of measures of patient-reported outcome (PRO) domains in chronic gout at the 2010 Outcome Measures in Rheumatology Meeting (OMERACT 10). METHODS: During the OMERACT 10 gout workshop, validation data were presented for key PRO domains including pain [pain by visual analog scale (VAS)], patient global (patient global VAS), activity limitation [Health Assessment Questionnaire-Disability Index (HAQ-DI)], and a disease-specific measure, the Gout Assessment Questionnaire version 2.0 (GAQ v2.0). Data were presented on all 3 aspects of the OMERACT filters of truth, discrimination, and feasibility. One PRO, health-related quality of life measurement with the Medical Outcomes Study Short-form 36 (SF-36), was previously endorsed at OMERACT 9. RESULTS: One measure for each of the 3 PRO of pain, patient global, and activity limitation was endorsed by > 70% of the OMERACT delegates to have appropriate validation data. Specifically, pain measurement by VAS was endorsed by 85%, patient global assessment by VAS by 73%, and activity limitation by HAQ-DI by 71%. GAQ v2.0 received 30% vote and was not endorsed due to several concerns including low internal consistency and lack of familiarity with the measure. More validation studies are needed for this measure. CONCLUSION: With the endorsement of one measure each for pain, patient global, SF-36, and activity limitation, all 4 PRO for chronic gout have been endorsed. Future validation studies are needed for the disease-specific measure, GAQ v2.0. Validation for PRO for acute gout will be the focus of the next validation exercise for the OMERACT gout group.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".