Outcomes of the 2019 GRAPPA Workshop on Continuous Composite Indices for the Assessment of Psoriatic Arthritis and Membership-recommended Next Steps
Bibliographic record
Abstract
OBJECTIVE: Improving the assessment of psoriatic arthritis (PsA) is a key purpose of the Group for Research and Assessment of Psoriasis and PsA (GRAPPA). Herein, we report the proceedings of the GRAPPA composites workshop at the 2019 GRAPPA annual meeting and the membership's recommended next steps. METHODS: A review of continuous composite measures was conducted in an introductory workshop, followed by 10 breakout group sessions and a final plenary session for feedback and voting. RESULTS: Participants included 154 members: 87 rheumatologists, 18 dermatologists, 2 rheumatologist/dermatologists, 12 patient research partners, 14 academics, 1 methodologist, and 20 industry members. Of voting members, 88.8% agreed a need exists for a continuous composite measure for routine practice, but only 62% were currently using a composite measure. Of these, 27% were using the 28-joint count Disease Activity Score (DAS), which is not a PsA-specific measure; 20% were using a PsA-specific measure such as PsA DAS (PASDAS), Composite Psoriatic Disease Activity Index (CPDAI), or Disease Activity Index for PsA (DAPSA). Members agreed that the existing measures were not feasible in their current forms (CPDAI 83%, PASDAS 82%, and DAPSA 47%) and that modification should be tested. The majority (76%) agreed that disease effect should be measured separately from disease activity. CONCLUSION: The GRAPPA membership supports the need for a continuous composite measure of disease activity for use in routine clinical care, the separate measurement of disease effect and activity, and the testing of modifications to candidate instruments rather than the development of new measures.
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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 | 0.079 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".