Defining treatment success in rosacea as ‘clear’ may provide multiple patient benefits: results of a pooled analysis
Bibliographic record
Abstract
BACKGROUND: Rosacea treatment success is usually defined as a score of 1 ('almost clear') or 0 ('clear') on the 5-point Investigator Global Assessment (IGA) scale. OBJECTIVE: To evaluate whether, after successful treatment, 'clear' subjects had better outcomes than 'almost clear' subjects. METHODS: A pooled analysis was performed on 1366 rosacea subjects from four randomized controlled trials with IGA before and after treatment (ivermectin, metronidazole or vehicle). Assessments included the Dermatology Life Quality Index (DLQI) questionnaire and subject assessment of rosacea improvement. In one trial, patients were followed after the treatment period to measure time to relapse (IGA score ≥2). RESULTS: At end of treatment, more 'clear' than 'almost clear' subjects had a clinically meaningful difference in DLQI (59% vs. 44%; p < .001) and a final DLQI score of 0-1 indicating no effect on quality of life (84% vs. 66%; p < .001). More 'clear' subjects reported an 'excellent' improvement in their rosacea (77% vs. 42%; p < .001). The median time to relapse was more than 8 months for 'clear' vs. 3 months for 'almost clear' subjects (p < .0001). CONCLUSIONS: Achieving an endpoint of 'clear' (IGA 0) vs. 'almost clear' (IGA 1) is associated with multiple positive patient outcomes, including delayed time to relapse.
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 | 0.044 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.047 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 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".