Current Status, Goals, and Research Agenda for Outcome Measures Development in Behçet Syndrome: Report from OMERACT 2014
Bibliographic record
Abstract
OBJECTIVE: There is an unmet need for reliable, validated, and widely accepted outcomes and outcome measures for use in clinical trials in Behçet syndrome (BS). Our report summarizes initial steps taken by the Outcome Measures in Rheumatology (OMERACT) vasculitis working group toward developing a core set of outcome measures for BS according to the OMERACT methodology, including the OMERACT Filter 2.0, and discussions during the first meeting of the BS working group held during OMERACT 12 (2014). METHODS: During OMERACT 12, some of the important challenges in developing outcomes for BS were outlined and discussed, and a research agenda was drafted. RESULTS: Among topics discussed were the advantages and disadvantages of a composite measure for BS that evaluates several organs/organ systems; bringing patients and physicians together for discussions about how to assess disease activity; use of organ-specific measures developed for other diseases; and the inclusion of generic, disease-specific, or organ-specific measures. The importance of incorporating patients' perspectives, concerns, and ideas into outcome measure development was emphasized. CONCLUSION: The planned research agenda includes conducting a Delphi exercise among physicians from different specialties that are involved in the care of patients with BS and among patients with BS, with the aim of identifying candidate domains and subdomains to be assessed in randomized clinical trials of BS, and candidate items for a composite measure. The ultimate goal of the group is to develop a validated and widely accepted core set of outcomes and outcome measures for use in clinical trials in BS.
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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 | 0.344 | 0.271 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".