Repigmentation in vitiligo: position paper of the Vitiligo Global Issues Consensus Conference
Bibliographic record
Abstract
The Vitiligo Global Issues Consensus Conference (VGICC), through an international e-Delphi consensus, concluded that 'repigmentation' and 'maintenance of gained repigmentation' are essential core outcome measures in future vitiligo trials. This VGICC position paper addresses these core topics in two sections and includes an atlas depicting vitiligo repigmentation patterns and color match. The first section delineates mechanisms and characteristics of vitiligo repigmentation, and the second section summarizes the outcomes of international meeting discussions and two e-surveys on vitiligo repigmentation, which had been carried out over 3 yr. Treatment is defined as successful if repigmentation exceeds 80% and at least 80% of the gained repigmentation is maintained for over 6 months. No agreement was found on the best outcome measure for assessing target or global repigmentation, therefore highlighting the limitations of e-surveys in addressing clinical measurements. Until there is a clear consensus, existing tools should be selected according to the specific needs of each study. A workshop will be conducted to address the remaining issues so as to achieve a consensus.
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 | 0.096 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.018 | 0.021 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".