Global publication productivity in dermatology: a bibliometric description of the past and estimation of the future
Bibliographic record
Abstract
BACKGROUND: In the past two centuries, generations of dermatologists around the world have created an enormous number of publications. To our knowledge, no bibliometric analysis of these publications has been performed so far, nor have registered trials been analysed to anticipate future publication trends. OBJECTIVES: To determine the global distribution of national publication productivity, most published topics, institutions and funding sources contributing most to publications and to anticipate future trends based on registered clinical trials. METHODS: Following pre-assessment on PubMed, Embase, Web of Science and Scopus, the number of publications for 'dermatology' was determined for each of 195 countries, normalized per 1 Mio inhabitants and bibliometrically analysed. Dermatology-related trials registered at clinicaltrials.gov were specified by the top-10 diagnoses for the top-10 countries. RESULTS: The search yielded 1 071 518 publications between 1832 and 2019 with the top-5 diagnoses being melanoma, basal cell carcinoma, psoriasis, pruritus/itch and atopic dermatitis. The top-3 countries with highest absolute numbers of publications were the USA (30.6%), Germany (8.1%) and the UK (8.1%), whereas Switzerland, Denmark and Sweden had the highest publication rates when normalized by inhabitants. The most productive affiliation was the Harvard Medical School, the leading funding source the National Institutes of Health. Currently, maximum number of trials are registered in the USA (8111), France (1543) and Canada (1368). The highest percentage of all dermatology-related trials in a specific country were as follows: Melanoma in the Netherlands (24.8%), psoriasis in Germany (21.7%) and atopic dermatitis in Japan (15.9%). CONCLUSION: The top-10 countries including the USA, Canada, a few European and Asian countries contributed more than 3/4 of all publications. The USA hold the dominant leader position both in past publication productivity and currently registered trials. While most Western countries continue to focus their research on the top-10 topics, China and India appear to prioritize their scope towards other topics.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.020 | 0.141 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".