Development of Composite Measures for Psoriatic Arthritis: A Report from the GRAPPA 2010 Annual Meeting
Bibliographic record
Abstract
Composite disease outcome measures have been used in rheumatology for some time, but a disease-specific composite measure for psoriatic arthritis (PsA) has not yet been validated. Currently, instruments developed for use in rheumatoid arthritis are employed in PsA and include the American College of Rheumatology response criteria (ACR20, 50, and 70) and the Disease Activity Score for 28 and 44 joints (DAS28 and DAS44); however, these instruments do not cover the full spectrum of psoriatic disease. A composite measure is one way of incorporating an assessment of all relevant clinical outcomes into one single measure. By definition, it incorporates several dimensions of disease status, often by combining these different domains into a single score, which in the case of PsA includes joints, skin, entheses, dactylitis, and axial disease. New indices that combine these diverse clinical manifestations of PsA are under development and, in some cases, in the validation phase. The Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) established the GRAPPA Composite Exercise (GRACE) project to compare existing and emerging composite measures and to develop a new index. At the GRAPPA 2010 meeting, initial results from this project were presented, and existing and new candidate measures were compared.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.048 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".