Atopic Dermatitis: A Guide to Transitioning to Janus Kinase Inhibitors
Bibliographic record
Abstract
Janus kinase inhibitors (JAKis) are promising medications that the Food and Drug Administration recently approved for treatment of atopic dermatitis in January 2022. These medications offer a novel therapeutic mechanism and may be an additional treatment avenue for patients who are currently reliant on conventional immunosuppressants, such as cyclosporine A, methotrexate, or mycophenolate mofetil, or newer medications, such as dupilumab. However, redundant treatment puts patients at risk for excessive toxicity and polypharmacy, whereas abrupt tapering of a preexisting regimen may cause flares of the disease. Thus, transitioning to JAKis should be implemented strategically to retain the therapeutic benefit and minimize the risk of flares. Herein, we outline gradual transition schemas for patients needing to transition to JAKis from conventional immunosuppressants or dupilumab. There is no evidence-based guideline to instruct this transition to JAKis, and our recommendations are based on expert experience and the review of efficacy data from pivotal trials.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.017 | 0.021 |
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".