DOZ047.88: Esophageal atresia: a scientometric analysis of the global research architecture and collaborative networks
Bibliographic record
Abstract
Abstract Background Esophageal atresia (EA) and tracheoesophageal fistula (TEF) represent a spectrum of relatively rare and complex malformations, which remain a major therapeutic challenge for most involved specialists. Whereas the number of EA/TEF-related publications is constantly growing, no thorough assessment of the global research architecture has been performed yet. Hence, this study aimed to critically evaluate the scientific EA/TEF activities in relation to geographical developments and existing research networks using a combination of scientometric methodologies and visualization tools. Methods A comprehensive search strategy for the Web of Science™ database was designed to retrieve bibliographic data on scientific EA/TEF publications for the time span between January 1900 and December 2018. The total reseach output of countries, institutions, individual authors, and collaborative networks was analzyed. Semiqualitative research measures, including citation rate and h-index, were assessed. Choropleth mapping and network diagrams were employed to visualize results. Results A total of 4586 publications on EA/TEF were identified, originating from 86 countries (79.0% written in English). The largest number was published by the USA (n = 799; 17.4%), the UK (n = 260; 5.7%), and Canada (n = 190; 4.1%). The USA produced the highest number of co-operative articles (n = 73) and the most productive collaborative networks were established between USA/Canada (n = 22), USA/Netherlands (n = 19), and USA/Germany (n = 13). Scientific papers from the UK received the highest average citation rate, with 17.6 citations per item, whereas the USA, with 47, had the highest country-specific h-index. Eighty-two articles were published under the auspices of multicenter research consortiums and national study groups. The most productive institutions and authors were based in the UK, the USA, France, Canada, Australia, the Netherlands, Finland, and Spain. Conclusions This is the first in-depth analysis of the worldwide EA/TEF research activity, offering unique insights into the global scientific landscape in this field. Over the past decades, EA/TEF research has increasingly become multidisciplinary but the main research endeavors continue to be concentrated in a few high-income countries. International EA/TEF collaborations and translational research should be strengthened to foster true scientific progress with this rare condition.
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 | 0.012 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.090 | 0.151 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".