Rosacea Core Domain Set for Clinical Trials and Practice
Bibliographic record
Abstract
Importance: Inconsistent reporting of outcomes in clinical trials of rosacea is impeding and likely preventing accurate data pooling and meta-analyses. There is a need for standardization of outcomes assessed during intervention trials of rosacea. Objective: To develop a rosacea core outcome set (COS) based on key domains that are globally relevant and applicable to all demographic groups to be used as a minimum list of outcomes for reporting by rosacea clinical trials, and when appropriate, in clinical practice. Evidence Review: A systematic literature review of rosacea clinical trials was conducted. Discrete outcomes were extracted and augmented through discussions and focus groups with key stakeholders. The initial list of 192 outcomes was refined to identify 50 unique outcomes that were rated through the Delphi process Round 1 by 88 panelists (63 physicians from 17 countries and 25 patients with rosacea in the US) on 9-point Likert scale. Based on feedback, an additional 11 outcomes were added in Round 2. Outcomes deemed to be critical for inclusion (rated 7-9 by ≥70% of both groups) were discussed in consensus meetings. The outcomes deemed to be most important for inclusion by at least 85% of the participants were incorporated into the final core domain set. Findings: The Delphi process and consensus-building meetings identified a final core set of 8 domains for rosacea clinical trials: ocular signs and symptoms; skin signs of disease; skin symptoms; overall severity; patient satisfaction; quality of life; degree of improvement; and presence and severity of treatment-related adverse events. Recommendations were also made for application in the clinical setting. Conclusions and Relevance: This core domain set for rosacea research is now available; its adoption by researchers may improve the usefulness of future trials of rosacea therapies by enabling meta-analyses and other comparisons across studies. This core domain set may also be useful in clinical practice.
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How this classification was reachedexpand
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".