Advancing the Development of Patient-reported Outcomes for Adult Myositis at OMERACT 2016: An International Delphi Study
Bibliographic record
Abstract
OBJECTIVE: To define a set of core patient-reported domains and respective instruments for use in idiopathic inflammatory myopathies (IIM). Previously, we reported a systematic literature review on patient-reported outcomes (PRO) in IIM followed by conducting international focus groups to elicit patient perspectives of myositis symptoms and effects. METHODS: Based on qualitative content analysis of focus groups, an initial list of 26 candidate domains was constructed. We subsequently conducted an international modified Delphi survey to identify the importance of each of the 26 domains. Participants were asked to rate each domain on a scale of 0-10 (0 = not important, 10 = very important). RESULTS: In this first round of the Delphi survey, 643 patients participated from the United States (n = 543), Sweden (n = 49), and South Korea (n = 51). Of the 26 domains, 19 (73%) were rated of high importance (≥ 7/10). The top 5 domains were muscle symptoms, fatigue, interactions with healthcare, medication side effects, and pain. During Outcome Measures in Rheumatology (OMERACT) 2016, we discussed the goal for ultimate reduction in the number of domains and the importance of considering representation of healthcare providers from other specialties, caregivers, representatives of pharmaceutical industries, and regulatory authorities in the next rounds of Delphi to represent broader perspectives on IIM. CONCLUSION: Further prioritization and a reduction in the number of domains will be needed for the next Delphi. At the next biennial OMERACT meeting, we aim to present and seek voting on a Myositis Preliminary PRO Core Set to enable ultimate measure selection and development.
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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 | 0.256 | 0.170 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".