Development of a Disease Severity and Responder Index for Psoriatic Arthritis (PsA) — Report of the OMERACT 10 PsA Special Interest Group
Bibliographic record
Abstract
Work within the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) to develop and validate composite disease activity measures in PsA has progressed. At the Outcome Measures in Rheumatology Clinical Trials (OMERACT) 8 meeting, a core set of domains to be assessed in randomized controlled trials (RCT) and longitudinal observational studies (LOS) of PsA was agreed upon. At OMERACT 10, work to date regarding proposed composite responder indices was presented. Five proposed composite responder definitions for PsA were reviewed and discussed including new data from the GRACE (GRAppa Composite Exercise) study. There was agreement that the work to date was promising, and that developing composite outcome measures for use in RCT and LOS was important. Further work was required, including data on followup timepoints and less common phenotypes of PsA, to ensure that all subgroups were represented within GRACE. During discussion on the concept of composite measures for PsA, based on predominant/little/no involvement in several domains (such as skin versus joints, enthesitis, dactylitis, spondyloarthritis) it was acknowledged that a simple summative score encompassing all domains of PsA would be difficult to construct psychometrically and may not be appropriate. Ideally, any composite measure should retain the ability to differentiate between activity in individual domains, such as enthesitis or skin psoriasis, so that the influence of each can be assessed independently. Further work is required within the GRACE dataset to develop an optimal composite measure for PsA. Several proposals to date have shown preliminary validity according to the OMERACT filter.
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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 | 0.076 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| 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".