Bibliometric analysis of pediatric extracorporeal membrane oxygen literature
Bibliographic record
Abstract
Aim: Consistent innovation is essential given the crucial necessity of extracorporeal membrane oxygenation (ECMO) in patients with respiratory and cardiovascular illnesses that are life-threatening. In the literature so far no bibliometric analysis on pediatric ECMO has been done. We aim to undertake a bibliometric review of the ECMO literature to inform evidence-based innovation, emphasizing key trends and advancements in pediatric ECMO. Materials and Methods: The Web of Science (WoS) database was used to retrieve articles about pediatric ECMO that were published between 1970 and 2022, and these articles were then examined using statistical and bibliometric techniques. Effective analysis and trending topics were identified using network visualization maps. Results: The search for pediatric ECMO literature turned up 726 documents. The publications received 7984 total citations, with an average of 11 citations per publication. The Hirsch (H)-Index was 45, and articles made up 55.51% of the publications. The pediatric ECMO papers received an average of 18.03 citations per article, for a total of 7267 and with 44 H-index. The highest publishing year was 2020 (n=80). The majority of the articles (67.906%) originated in the United States. The other top 5 nations were Canada (4.132%), Italy (3.581%), Australia (3.168%), China (3.030%), and Germany (2.893%). Conclusion: This article can be a useful resource for investigators concerning global output for pediatric ECMO use. The United States ranked first in the number of publications and citations. With the increasing number of publications and implementation, ECMO needs to become more widespread around the world.
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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 | 0.006 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.154 | 0.158 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".