Development of Outcome Measures for Large-vessel Vasculitis for Use in Clinical Trials: Opportunities, Challenges, and Research Agenda
Bibliographic record
Abstract
Giant cell (GCA) and Takayasu's arteritis (TAK) are 2 forms of large-vessel vasculitis (LVV) that involve the aorta and its major branches. GCA has a predilection for the cranial branches, while TAK tends to affect the extracranial branches. Both disorders may also cause nonspecific constitutional symptoms. Although some clinical features are more common in one or the other disorder and the ages of initial presentation differ substantially, there is enough clinical and histopathologic overlap between these disorders that some investigators suggest GCA and TAK may be 2 processes within the spectrum of a single disease. There have been few randomized therapeutic trials completed in GCA, and none in TAK. The lack of therapeutic trials in LVV is only partially explained by the rarity of these diseases. It is likely that the lack of well validated outcome measures for LVV and uncertainties regarding trial design contribute to the paucity of trials for these diseases. An initiative to develop a core set of outcome measures for use in clinical trials of LVV was launched by the international OMERACT Vasculitis Working Group in 2009 and subsequently endorsed by the OMERACT community at the OMERACT 10 meeting. Aims of this initiative include: (1) to review the literature and existing data related to outcome assessments in LVV; (2) to obtain the opinion of experts and patients on disease content; and (3) to formulate a research agenda to facilitate a more data-based approach to outcomes development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".