International patient and physician consensus on a psoriatic arthritis core outcome set for clinical trials
Bibliographic record
Abstract
OBJECTIVE: To identify a core set of domains (outcomes) to be measured in psoriatic arthritis (PsA) clinical trials that represent both patients' and physicians' priorities. METHODS: We conducted (1) a systematic literature review (SLR) of domains assessed in PsA; (2) international focus groups to identify domains important to people with PsA; (3) two international surveys with patients and physicians to prioritise domains; (4) an international face-to-face meeting with patients and physicians using the nominal group technique method to agree on the most important domains; and (5) presentation and votes at the Outcome Measures in Rheumatology (OMERACT) conference in May 2016. All phases were performed in collaboration with patient research partners. RESULTS: We identified 39 unique domains through the SLR (24 domains) and international focus groups (34 domains). 50 patients and 75 physicians rated domain importance. During the March 2016 consensus meeting, 12 patients and 12 physicians agreed on 10 candidate domains. Then, 49 patients and 71 physicians rated these domains' importance. Five were important to >70% of both groups: musculoskeletal disease activity, skin disease activity, structural damage, pain and physical function. Fatigue and participation were important to >70% of patients. Patient global and systemic inflammation were important to >70% of physicians. The updated PsA core domain set endorsed by 90% of OMERACT 2016 participants includes musculoskeletal disease activity, skin disease activity, pain, patient global, physical function, health-related quality of life, fatigue and systemic inflammation. CONCLUSIONS: The updated PsA core domain set incorporates patients' and physicians' priorities and evolving PsA research. Next steps include identifying outcome measures that adequately assess these domains.
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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 | 0.468 | 0.447 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.012 |
| Bibliometrics | 0.018 | 0.010 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.011 | 0.016 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".