Biliary atresia: a scientometric analysis of the global research architecture and scientific developments
Bibliographic record
Abstract
Biliary atresia (BA) is a rare cholangiopathy of largely unknown etiology and unpredictable outcome. There has been an increasing number of BA-related publications, which may challenge researchers to determine their actual scientific value. This study aimed to evaluate the global research activity and developments relating to BA using a combination of scientometric methodologies and visualization tools. A comprehensive search strategy for the Web of Science™ database was designed to obtain bibliographic data on scientific BA publications for the timespan 1900-2018. Research output of countries, institutions, individual authors and collaborative networks was analyzed. Semi-qualitative research measures including citation rate and h-index were assessed. Choropleth mapping and network diagrams were used to visualize results. In total, 4,459 publications on BA were identified (88.5% in English), originating from 63 countries. The largest number was published by the USA (n = 991; 22.2%), Japan (n = 667; 15.0%) and the UK (n = 294; 6.6%). The USA combined the highest number of cooperation articles (n = 140). The most productive collaborative network was established between the USA and Canada (n = 17). Scientific papers from the UK received the highest average citation rate (16.7), whereas the USA had the highest country-specific h-index (59). Eighty-eight (2.0%) items were published under the auspices of multicenter consortiums and registries. The most productive institutions and authors were based in the USA, the UK, Japan, France, Canada and Taiwan. BA-related research has constantly been progressing, becoming more multidisciplinary but with main research endeavors concentrated in a few high-income countries. Studies into pathogenesis of BA remain uncommon, but are sorely needed to foster true scientific progress with this rare disease. Hence, international collaborative and translational research should be strengthened to allow further evolution in this field.
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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 | 0.019 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.133 | 0.178 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| 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".