Practical Recommendations on Laboratory Monitoring in Patients with Atopic Dermatitis on Oral JAK Inhibitors
Bibliographic record
Abstract
Oral Janus kinase inhibitors (JAKi), a class of advanced targeted systemic therapy, have demonstrated efficacy and safety in the treatment of moderate-to-severe atopic dermatitis (AD). Like other small molecules, oral JAKi have the potential for off-target effects including laboratory-related adverse events (AEs). Product labels for oral JAKi recommend an initial laboratory assessment and follow-up 4-12 weeks later to monitor for potential changes, based on evidence from clinical trials across therapeutic indications for oral JAKi, which may not reflect a population of moderate-to-severe AD patients typically seen in routine clinical practice. To address this gap, a panel of eight dermatologists with clinical and research experience with oral JAKi for the management of AD conducted a targeted review of the literature focused on key laboratory-related AEs associated with oral JAKi in the moderate-to-severe AD population. Based on the synthesis of evidence and informed opinion, a set of best practice statements related to fundamental standards of care and consensus recommendations on laboratory monitoring were suggested, and level of agreement was ascertained using a Likert scale from 0 to 100. There was a high level of agreement on three of the four suggested recommendations related to assessment and monitoring of key laboratory parameters and to dose reduction or switching in response to laboratory changes; there was a lower level of agreement related to the frequency of ongoing laboratory monitoring. Appropriate patient selection and laboratory assessment is an important strategy to mitigate the potential risks associated with oral JAKi when treating AD.
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 | 0.062 | 0.185 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.010 | 0.011 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".