Update on Outcome Measure Development for Large Vessel Vasculitis: Report from OMERACT 12
Bibliographic record
Abstract
OBJECTIVE: The rarity of large vessel vasculitis (LVV) is a major factor limiting randomized controlled trials in LVV, resulting in treatment choices in these diseases that are guided mainly by observational studies and expert opinion. Further complicating trials in LVV is the absence of validated and meaningful outcome measures. The Outcome Measures in Rheumatology (OMERACT) vasculitis working group initiated the Large Vessel Vasculitis task force in 2009 to develop data-driven, validated outcome tools for clinical investigation in LVV. This report summarizes the progress that has been made on a disease activity assessment tool and patient-reported outcomes in LVV as well as the group's research agenda. METHODS: The OMERACT LVV task force brought an international group of investigators and patient research partners together to work collaboratively on developing outcome tools. The group initially focused on disease activity assessment tools in LVV. Following a systematic literature review, an international Delphi exercise was conducted to obtain expert opinion on principles and domains for disease assessment. The OMERACT vasculitis working group's LVV task force is also conducting qualitative research with patients, including interviews, focus groups, and engaging patients as research partners, all to ensure that the approach to disease assessment includes measures of patients' perspectives and that patients have input into the research agenda and process. RESULTS: The preliminary results of both the Delphi exercise and the qualitative interviews were discussed at the OMERACT 12 (2014) meeting and the completion of the analyses will produce an initial set of domains and instruments to form the basis of next steps in the research agenda. CONCLUSION: The research agenda continues to evolve, with the ultimate goal of developing an OMERACT-endorsed core set of outcome measures for use in clinical trials of LVV.
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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 | 0.205 | 0.294 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".