The Leading Authors in Three High Impact Dermatology Journals
Bibliographic record
Abstract
Recently in dermatology, the most influential scientists were reported [1,2].Similarly, after analyzing the publications over 10 years (2011)(2012)(2013)(2014)(2015)(2016)(2017)(2018)(2019)(2020), the top authors in the Journal of the American Academy of Dermatology (JAAD) were also described [3].In this letter, we are reporting for the first time the top three authors in three world-class journals (ie, JAAD, JAMA Dermatology [JAMA-D], and American Journal of Clinical Dermatology [AJCD]).On July 24, 2022, the data was retrieved from the Scopus database, and the analysis was performed on RStudio (Bibliometrix/Biblioshiny) software (RStudio, PBC).We only analyzed research articles and excluded the year 2022.Scopus has been covering JAAD, JAMA-D and AJCD since 1979, 2013 and 2000, respectively.In total:• JAAD published 17,065 research articles.A total of 93 authors published at least 30 articles (for a total of 2732 articles).In these publications, the authors were from 2307 universities in 65 countries.
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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 | 0.011 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.020 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.020 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.085 | 0.032 |
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".