A CollaboraTive Initiative of worldwide Vitiligo Experts and patients to define vitiligo activity (ACTIVE): study protocol
Bibliographic record
Abstract
OBJECTIVES: xperts and patients to define vitiligo activity (ACTIVE) project aims to develop standardized definitions and criteria for assessing disease activity in vitiligo using a consensus-based, multi-stakeholder approach. METHODS: The project is divided into three main topics. Topic 1 focuses on standardizing the definition and terminology of clinical signs of disease activity, using literature reviews, iterative e-Delphi surveys, and a consensus meeting. Topic 2 aims to classify disease activity into distinct activity categories by differentiating between slowly and highly progressive vitiligo. This will involve a literature search, a patient focus group, an e-Delphi survey, and a final consensus meeting. Topic 3 addresses remaining challenges in defining and assessing disease activity in vitiligo. This will include, for instance, the definitions of stability versus activity. In addition, unresolved issues and feedback identified by working groups 1 and 2 will further contribute to working group 3 with the aim of reaching a global consensus on all other key concepts of disease activity in vitiligo. RESULTS/CONCLUSION: By engaging international vitiligo experts and patients/patient representatives throughout the process, the ACTIVE study is designed to enhance consistency in disease activity definitions across multiple centers. This will improve the comparability of outcomes, facilitate management, and support clinical trials evaluating new treatments with more reliable inclusion.
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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 | 0.118 | 0.063 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".