Global research output on Kawasaki disease: A scientometric assessment during 2005-14
Bibliographic record
Abstract
The present paper attempts to study the performance of global research on Kawasaki disease using a series of bibliometric indicators. As seen from Scopus database the global research output cumulated to 2717 publications in 10 years during 2005-14, with annual average growth rate of 7.09% and citation impact per paper of 8.45. Around 68% of the global publications on Kawasaki disease were cited one or more times. The top 10 most productive countries together accounted for 74.35% share of the global output during 2005-14, with Canada registering the largest (2.44) relative citation index, followed by USA (1.79), Japan (1.47), France (1.45), Taiwan (1.36) and Italy (1.03) during 2005-14. Medicine contributed the largest publications share of 93.89%, followed by immunology & microbiology (10.78%), biochemistry, genetics & molecular biology (7.95%), pharmacology, toxicology & pharmaceutics (1.77%), neurosciences (1.18%) and agricultural & biological sciences (1.14%) during 2005-14. The top 21 and 20 most productive global organizations and authors together contributed 26.76% and 25.98% publication share and 58.55% and 83.44% citation share to the world publications and citation output on Kawasaki disease during 2005-14. The top 20 journals together accounted for 30.95% share of the total global publication output during 2005-14. There were 48 high cited papers, which received 60 or more citations and together received 5519 citations, registering the average citation per paper of 114.98 during 2005-14. The authors suggest the need for developing national policy and guidelines for diagnosis, treatment and management of Kawasaki disease patients.
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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.055 | 0.114 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".