Latest Research Hot Spots of Atopic Dermatitis Management Using Janus Kinase Inhibitor: A Bibliometric Analysis and Visualized Review
Bibliographic record
Abstract
Houriah Nukaly,1 Deemah S AlHuraish,2 Basel H Bakhamees,3 Haya A AlHemli,2 Mai S Alsaadoon,4 Renad K Alhejji,4 Ghaid Alotaibi,5 Shikhah Abdullah Alomran,2 Zeinah AlHalees6 1Division of Experimental Medicine, McGill University Health Centre, Montréal, Québec, Canada; 2College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Eastern Province, Saudi Arabia; 3Faculty of Medicine, King Abdulaziz University, Jeddah, 23523, Saudi Arabia; 4College of Medicine, King Faisal University, Al Hofuf, Saudi Arabia; 5College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia; 6King Faisal Specialist Hospital and Research Center, Riyadh, Saudi ArabiaCorrespondence: Houriah Nukaly, Division of Experimental Medicine, McGill University Health Centre, 1650 Cedar Avenue, Montreal, QC, H3G 1A4, Canada, Email houriyahnukaly@gmail.comAbstract: Atopic dermatitis (AD) affects 5– 20% of the global population, with moderate-to-severe cases frequently requiring systemic therapy. The introduction of Janus kinase inhibitors (JAKi) has transformed therapeutic options, warranting a comprehensive analysis of the evolving research landscape. This bibliometric and visualized review aimed to identify global research hotspots, collaboration networks, and influential contributors in JAKi-related AD research. Publications were retrieved from the Web of Science Core Collection (2014– 2024) using the terms “(Janus kinase inhibitors OR JAK) AND (atopic dermatitis)”. Of 797 publications identified, 776 met inclusion criteria. Bibliometric mapping and visualization were conducted with VOSviewer, Excel, and Draw.io. The United States produced the most publications (34%), followed by Germany (12.62%), Japan (11.87%), and China (8.5%). The Icahn School of Medicine at Mount Sinai and Oregon Health & Science University led institutional output, while Kyoto University demonstrated the highest citation impact (82.57 citations per publication). Among authors, Emma Guttman-Yassky (25 publications) and Eric Simpson (22 publications) were the most prolific. Journal of Dermatological Treatment and Journal of the European Academy of Dermatology and Venereology were the leading publishing journals, while The Journal of Allergy and Clinical Immunology was the most co-cited. The most frequently cited reference was Oetjen (2017), with 674 citations. Keyword analysis highlighted “atopic dermatitis”, “JAK inhibitors”, “upadacitinib”, and “baricitinib” as central themes, with abrocitinib and biologics emerging as newer hotspots. This study provides an updated overview of global research activity on JAK inhibitors in AD, addressing knowledge gaps, collaboration patterns, and future directions in targeted therapy.Keywords: atopic dermatitis, janus kinase inhibitors, JAK inhibitors, upadacitinib, baricitinib, global research trends
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.059 | 0.068 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".