Scientific publications in urology and nephrology journals from China: A 10 year analysis
Bibliographic record
Abstract
BACKGROUND: The scientific research in urology and nephrology of China has developed significantly. The present study was designed to analyze the outputs of publications in urology and nephrology journals from three regions of China: mainland, Taiwan and Hong Kong. METHODS: The numbers of articles, impact factors, citation reports and other indexes within this category between 2000 and 2009 were extracted for quantity and quality comparisons from PubMed and the ISI (Institute for Scientific Information-currently called the Thomson Reuters Web of Knowledge) database. RESULTS: There were 3100 articles from the mainland (36.5%), Taiwan (46.8%) and Hong Kong (16.7%), and the increasing trend in each region was significant (p < 0.001). The accumulated impact factor and total citation of Taiwan exceeded the other two regions, while the average impact factor and citation of Hong Kong was highest. There were differences between the three regions on the most popular journals. INTERPRETATION: Although the quantity of articles in urology and nephrology from the mainland has exceeded Taiwan and Hong Kong since 2008, there is a considerable gap in the quality of articles between the mainland and the other two regions.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.029 | 0.035 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".