Sustained hair regrowth with continued ritlecitinib treatment through week 48 in patients with alopecia areata with or without early target responses: Post hoc analysis of the ALLEGRO phase 2b/3 trial
Bibliographic record
Abstract
BACKGROUND: Few treatments for alopecia areata have demonstrated sustained efficacy. OBJECTIVE: Evaluate the efficacy and safety of continued ritlecitinib treatment to week 48 in patients with alopecia areata with or without target efficacy responses at week 24. METHODS: Patients aged ≥12 years received daily ritlecitinib (±4-week loading dose): 200/50 mg, 200/30 mg, 50 mg, or 30 mg. Patients with clinical response at week 24, based on a Severity of Alopecia Tool (SALT) score ≤20 and ≤10, were evaluated for sustained response through week 48. Nonresponders at week 24 were assessed for response through week 48. RESULTS: Among ritlecitinib-treated patients with SALT score ≤20 and ≤10 responses at week 24, ≥85% and ≥68%, respectively, sustained these responses through week 48. Of those with a SALT score >20 at week 24, 22% to 34% achieved a SALT score ≤20 at week 48. Of those with a SALT score >10 at week 24, 20% to 26% achieved a SALT score ≤10 at week 48. Safety was similar across subgroups. LIMITATIONS: Small sample size. CONCLUSION: Hair regrowth was sustained through week 48 in patients with response at week 24. Up to one-third of patients who did not meet target efficacy at week 24 achieved response with continued ritlecitinib treatment.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".