Growing Trend of China’s Contribution to the Field of Rheumatology 2000–2009: A Survey of Chinese Rheumatology Research
Bibliographic record
Abstract
OBJECTIVE: In the past decade, rheumatology in China has achieved great advances. However, scientific publications on rheumatology in the 3 major regions of China - Mainland (ML), Hong Kong (HK), and Taiwan (TW) - are unknown. We assessed the performance of rheumatology research in China from 2000 to 2009. METHODS: Twenty-two journals included in the rheumatology category of the Journal Citation Reports database were selected. We analyzed the following measures for 2000-2009: (1) total number of articles originating from ML, HK, and TW; (2) impact factor (IF) of those articles; (3) total number of citations and average number of citations per article; and (4) number of articles about clinical trials, randomized controlled trials (RCT), and case reports. We also noted the total number of articles from the 3 regions published in 10 top-ranking journals. RESULTS: There were 788 articles for the 3 regions of China, including 259 from ML, 372 from TW, and 157 from HK, with a positive trend between the years 2000 to 2009. From 2006 on, published articles from ML exceeded those from HK, and in 2008, published articles from ML exceeded those from TW. HK had the highest average IF and highest average citations of each article compared with articles from ML and TW. TW published the most RCT, clinical trials, and case reports, as well as the most articles in the 10 top-ranking journals in the last decade, followed by ML and HK. CONCLUSION: Chinese contributions to the field of rheumatology have increased rapidly since 2000, particularly from ML. HK had the highest quality research output according to average IF and average citations per article.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".