Core Set of Domains for Outcome Measures in Behçet's Syndrome.
Bibliographic record
Abstract
OBJECTIVE: An unmet need exists for reliable, validated, and widely-accepted outcome measures for randomized clinical trials in Behçet's syndrome. The Outcome Measures in Rheumatology (OMERACT) Behçet's Syndrome Working Group, a large, multidisciplinary group of experts in Behçet's syndrome and patients with Behçet's syndrome, had an objective of developing a core set of data-driven outcome measures for use in all clinical trials of Behçet's syndrome. METHODS: The core domain set was developed through a comprehensive, iterative, multistage project that included a systematic review, a focus group meeting and qualitative patient interviews, a survey among experts in Behçet's syndrome, a Delphi exercise involving both patients and physician experts in Behçet's syndrome, and use of the data, insight, and feedback generated by these processes to develop a final core domain set. RESULTS: All steps were completed and domains were delineated across the organ systems involved in this disease. Since trials in Behçet's syndrome often focus on specific manifestations and not on the disease in its entirety, the final proposed core set includes 5 domains mandatory for study in all trials in Behçet's syndrome (disease activity, new organ involvement, quality of life, adverse events, and death) with additional subdomains mandatory for study of specific organ-systems. The final core set was endorsed at the 2018 OMERACT meeting. CONCLUSION: The core set of domains in Behçet's syndrome provides the foundation through which the international research community, including clinical investigators, patients, the biopharmaceutical industry, and government regulatory bodies can harmonize the study of this complex disease, compare findings across studies, and advance development of effective therapies.
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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 | 0.134 | 0.188 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".