Development of a Core Set of Outcome Measures for Large-vessel Vasculitis: Report from OMERACT 2016
Bibliographic record
Abstract
OBJECTIVE: Among the challenges in conducting clinical trials in large-vessel vasculitis (LVV), including both giant cell arteritis (GCA) and Takayasu arteritis (TA), is the lack of standardized and meaningful outcome measures. The Outcome Measures in Rheumatology (OMERACT) Vasculitis Working Group initiated an international effort to develop and validate data-driven outcome tools for clinical investigation in LVV. METHODS: An international Delphi exercise was completed to gather opinions from clinical experts on LVV-related domains considered important to measure in trials. Patient interviews and focus groups were completed to identify outcomes of importance to patients. The results of these activities were presented and discussed in a "Virtual Special Interest Group" using telephone- and Internet-based conferences, discussions through electronic mail, and an in-person session at the 2016 OMERACT meeting. A preliminary core set of domains common for all forms of LVV with disease-specific elements was proposed. RESULTS: The majority of experts agree with using common outcome measures for GCA and TA, with the option of supplementation with disease-specific items. Following interviews and focus groups, pain, fatigue, and emotional effect emerged as health-related quality of life domains important to patients. Current disease assessment tools, including the Birmingham Vasculitis Activity Score, were found to be inadequate to assess disease activity in GCA and standardized assessment of imaging tests were felt crucial to study LVV, especially TA. CONCLUSION: Initial data from a clinician Delphi exercise and structured patient interviews have provided themes toward an OMERACT-endorsed core set of domains and outcome 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 | 0.260 | 0.232 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".