Long-term Efficacy and Safety of Ritlecitinib in Adults and Adolescents with Alopecia Areata: 3-year Results from the ALLEGRO-LT Phase 3, Open-label Study
Bibliographic record
Abstract
Introduction: We report efficacy results of ritlecitinib up to 3 years in patients with AA from the ALLEGRO phase 2b/3 (NCT03732807) and ongoing, phase 3, open-label ALLEGRO-LT (NCT04006457) studies. Methods: Patients aged ≥12 years with AA and ≥50% scalp hair loss who received daily ritlecitinib 50-mg in ALLEGRO-2b/3 and rolled over to ALLEGRO-LT (continued to receive 50-mg) were included. Patients who received placebo and switched to 50-mg were re-baselined to align time points and across groups. Visits were calculated as time since the first ritlecitinib dose, thus resulting in different months for some visits. Observed and last observation carried forward [LOCF]) data are reported at the time of data cutoff (June 25th, 2024). Results: 191 patients were included. At 3 years, 65.1% (71/109 observed) and 47.1% (90/191 LOCF) of patients had Severity of Alopecia Tool (SALT) score ≤20. SALT score ≤10 response rates were 52.3% (57/109 observed) and 36.7% (70/191) (LOCF). Patients’ Global Impression of Change response (“moderately” or “greatly” improved) rates at 3 years were 68.4% (52/76) (observed) and 55.6% (105/189) (LOCF). Conclusions: Ritlecitinib 50-mg demonstrated clinically meaningful clinician- and patient-reported efficacy up to 3 years. These data are the longest duration of ritlecitinib treatment to be reported to date and support the long-term use of ritlecitinib in patients aged ≥12 years with AA.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".