Abrocitinib Treatment Modes: Impact on Prognosis and Relapse Rates in Atopic Dermatitis
Bibliographic record
Abstract
Abstract: Background: Although clinical studies have demonstrated the effectiveness and safety of abrocitinib for moderate to severe atopic dermatitis (AD), real-world evidences are limited. In particular, studies exploring the impact of different treatment modes on prognosis are currently lacking. Objective: This study aimed to investigate the effects of various abrocitinib treatment regimens on the prognosis of AD. Methods: A retrospective study was conducted at the Southwest Hospital of the Army Military Medical University and involved patients with moderate to severe AD receiving abrocitinib. After disease control, patients were given the option to continue, taper, or discontinue abrocitinib based on their preferences. Clinical data from eligible patients were retrospectively collected between August 2023 and April 2024. Results: In the maintenance group (100 mg/day), EASI-75, and pp-NRS4 were achieved by 33.3% and 58.3% of patients, respectively, with a mean reduction of 12.8 points in Dermatology Quality of Life Index (DLQI) compared with baseline. Patients who completed the 12-week induction period (including both maintenance and tapering groups) showed greater improvement in SCORing atopic dermatitis ( P < 0.0001; P = 0.0002), Eczema Area and Severity Index ( P < 0.0001; P = 0.0002), Peak Pruritus Numerical Rating Scale (all P < 0.0001), and DLQI (all P < 0.0001), as well as a longer time to relapse, compared to those in the discontinuation group. Conclusion: Continuous treatment with abrocitinib and completion of the 12-week induction period were associated with improved outcomes and reduced relapse rates in AD patients.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".