Outcome Measures Used in Clinical Trials for Behçet Syndrome: A Systematic Review
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".