A Plain Language Summary on Ritlecitinib Treatment for Adults and Adolescents with Alopecia Areata
Bibliographic record
Abstract
WHAT IS THIS SUMMARY ABOUT?: . ALLEGRO-2b/3 looked at how well and safely the study medicine, ritlecitinib, works in treating people with alopecia areata ('AA' for short). The immune system protects your body from outside invaders such as bacteria and viruses. AA is an autoimmune disease, meaning a disease in which one's immune system attacks healthy cells of the body by mistake. In AA, the immune system attacks hair follicles, causing hair to fall out. AA causes hair loss ranging from small bald patches to complete hair loss on the scalp, face, and/or body. Ritlecitinib is a medicine taken as a pill every day, by mouth, that is approved for the treatment of severe AA. It blocks processes that are known to play a role in causing hair loss in patients with AA. WHAT WERE THE RESULTS OF THE STUDY?: Adults and adolescents (12 years and older) took part in the ALLEGRO-2b/3 study. They either took ritlecitinib for 48 weeks or took a placebo (a pill with no medicine) for 24 weeks. Participants taking placebo later switched to taking ritlecitinib for 24 weeks. The study showed that participants taking ritlecitinib had more hair regrowth on their scalp after 24 weeks than those taking the placebo. Hair regrowth was also seen on the eyebrows and eyelashes in participants taking ritlecitinib. Hair regrowth continued to improve to week 48 with continued ritlecitinib treatment. In addition, more participants taking ritlecitinib reported that their AA had 'moderately' or 'greatly' improved after 24 weeks than those taking the placebo. Similar numbers of participants taking ritlecitinib or placebo had side effects after 24 weeks. Most side effects were mild or moderate. WHAT DO THE RESULTS OF THE STUDY MEAN?: NCT03732807 (phase 2b/3 ALLEGRO study).
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".