Progress Towards a Core Set of Outcome Measures in Small-vessel Vasculitis. Report from OMERACT 9
Bibliographic record
Abstract
The past decade has seen a substantial increase in the number and quality of clinical trials of new therapies for vasculitis, including randomized, controlled, multicenter trials that have successfully incorporated measures of disease activity and toxicity. However, because current treatment regimens for severe disease effectively induce initial remission and reduce mortality, future trials will focus on any of several goals including: (a) treatment of mild-moderate disease; (b) prevention of chronic damage; (c) reduction in treatment toxicity; or (d) more subtle differences in remission induction or maintenance. Thus, new trials will require outcome measure instruments that are more precise and are better able to detect effective treatments for different disease states and measure chronic manifestations of disease. The OMERACT Vasculitis Working Group comprises international clinical investigators with expertise in vasculitis who, since 2002, have worked collaboratively to advance the refinement of outcome measures in vasculitis, create new measures to address domains of illness not covered by current research approaches, and harmonize outcome assessment in vasculitis. The focus of the OMERACT group to date has been on outcome measures in small-vessel vasculitis with an overall goal of creating a core set of outcome measures for vasculitis, each of which fulfills the OMERACT filter of truth, discrimination, feasibility, and identifying additional domains requiring further research. This process has been informed by several ongoing projects providing data on outcomes of disease activity, disease-related damage, multidimensional health-related quality of life, and patient-reported ratings of the burden of vasculitis.
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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 | 0.255 | 0.183 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".