Research interest in the usage of dupilumab for atopic dermatitis: a bibliometric analysis
Bibliographic record
Abstract
BACKGROUND: Recently, dupilumab has been used for the treatment of atopic dermatitis (AD), and growing interest in the subject has resulted in an increasing number of publications. OBJECTIVES: Our study aimed to evaluate the rapid progress, identify hot topics, and explore scientific advances and future trends in this field. MATERIALS & METHODS: The global distribution of publications was estimated with no time restrictions. Dupilumab as a treatment for AD was scanned in the Web of Science core collection using the topic terms "dupilumab" and "atopic Dermatitis". VOSviewer was applied for visualization of bibliometric analysis. Analysis of country and region distribution, impact of journal, authors, population, economic estimation among countries and regions, key words, as well as the top 20 cited articles were performed. RESULTS: In total, 910 publications were yielded from the Web of Science core collection database. Most studies were published in the USA (46.15%), Germany (17.91%), and France (14.07%); other countries included Denmark, the Netherlands, and Canada based on normalization of article numbers according to population and economic evaluation. Studies were most frequently reported in the British Journal of Dermatology and the Journal of the American Academy of Dermatology. Pirozzi, G. from France was the top-cited author. The most frequent key words were concepts in dermatology, allergy, and immunology. Remarkable landmark clinical trials were noted in the top 20 cited publications. CONCLUSION: The research of dupilumab for AD is rapidly developing. Countries in North America and Europe have remarkably contributed to researches of dupilumab as a treatment for AD. The bibliometric analysis also presents hallmark publications reporting scientific advances in therapy progress, which may provide a foundation for further research.
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 | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.126 | 0.152 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".