Composite Outcome Measures for Psoriatic Arthritis: OMERACT and 3 and 4 Visual Analog Scale Progress in 2023
Bibliographic record
Abstract
The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA)-Outcome Measures in Rheumatology (OMERACT) psoriatic arthritis (PsA) working group provided updates at the GRAPPA 2023 annual meeting on its work to evaluate composite outcome measures for PsA. An ongoing systematic literature review is in progress to evaluate psychometric measurement properties using the OMERACT filter 2.2 for a list of candidate composite outcome measures, which include minimal disease activity (MDA), Disease Activity for Psoriatic Arthritis (DAPSA), American College of Rheumatology (ACR) response criteria, Psoriatic Arthritis Disease Activity Score (PASDAS), Composite Psoriatic Disease Activity Index (CPDAI), 3 visual analog scale (3VAS), and 4VAS. The performance of the 3VAS and 4VAS in clinical practice and a synthesis of new data were presented, including estimates for minimal clinically important differences and thresholds of meaning, discrimination and construct validity, and longitudinal construct validity. Numeric rating scale (NRS) versions of the VAS have also been tested. Performance characteristics and psychometric properties are similar to the ASSESS study, a UK multicenter study, indicating that the VAS scales may be feasible tools for routine clinical care with a preference for the 4VAS because of superior face validity and clinical utility.
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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 | 0.037 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".