Comparison of publication trends in dermatology among Japan, South Korea and Mainland China
Bibliographic record
Abstract
BACKGROUND: We previously showed that the number of publications in dermatology is increasing year by year, and positively correlates with improved economic conditions in mainland China, a still developing Asian country. However, the characteristics of publications in dermatology departments in more developed Asian countries such as Japan and South Korea are unknown. METHODS: In the present study, publications from 2003 through 2012 in dermatology in Japan, South Korea and mainland China were characterized. All data were obtained from http://www.pubmed.com. RESULTS: Dermatology departments in Japan published 4,094 papers, while mainland China and South Korea published 1528 and 1,758 articles, respectively. 48% of articles from dermatology in Japan were original research and 36% were case reports; The number of publications in Japan remained stable over time, but the overall impact factors per paper increased linearly over the last 10 year period (p < 0.05). In mainland China, 67% of articles from dermatology were original research, while 19% were case reports; The number of publications and their impact factors per paper increased markedly. In South Korea, 65% of articles from dermatology were original research and 20% were case reports. The impact factors per paper remained unchanged, despite of the fact that the number of publications increased over the last 10 year period (r2 = 0.6820, p = 0.0032). Only mainland China showed a positive correlation of the number of publications with gross domestic product per capita during this study period. CONCLUSIONS: These results suggest that the total number of publications in dermatology correlates with economic conditions only in developing country, but not in more developed countries in Asia. The extent of economic development could determine both the publication quantity and quality.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.023 | 0.028 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".