Outcome Measures Used in Clinical Trials for Behçet Syndrome: A Systematic Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Behçet syndrome (BS) is a multisystem vasculitis that is most active during young adulthood, causing serious disability and significant impairment in quality of life. Differences in the disease course, severity, and organ involvement between patients, depending on the age at presentation and sex, makes it impossible to determine a single management strategy. The diversity and variability in the outcome measures used in clinical trials in BS makes it difficult to compare the results or inform physicians about the best management strategy for individual patients. There is a large unmet need to determine or develop validated outcome measures for use in clinical trials in BS that are acceptable to researchers and regulatory agencies. We conducted a systematic review to describe the outcomes and outcome measures that have been used in clinical trials in BS. This review revealed the diversity and variability in the outcomes and outcome measures and the lack of standard definitions for most outcomes and rarity of validated outcome tools for disease assessment in BS. This systematic literature review will identify domains and candidate instruments for use in a Delphi exercise, the next step in the development of a core set of outcome measures that are properly validated and widely accepted by the collaboration of researchers from many different regions of the world and from different specialties, including rheumatology, ophthalmology, dermatology, gastroenterology, and neurology.
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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 | 0.051 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.025 | 0.005 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it