Evolution and current state of global research on paediatric resuscitation: a systematic scientometric analysis
Bibliographic record
Abstract
BACKGROUND: Paediatric resuscitation is rare but potentially associated with maximal lifetime reduction. Notably, several nations experience high infant mortality rates even today. To improve clinical outcomes and promote research, detailed analyses on evolution and current state of research on paediatric resuscitation are necessary. METHODS: Research on paediatric resuscitation published in-between 1900 and 2019 were searched using Web of Science. Metadata were extracted and analyzed based on the science performance evaluation (SciPE) protocol. Research performance was evaluated regarding quality and quantity over time, including comparisons to adult resuscitation. National research performance was related to population, financial capacities, infant mortality rate, collaborations, and authors' gender. RESULTS: Similar to adult resuscitation, research performance on paediatric resuscitation grew exponentially with most original articles being published during the last decade (1106/1896). The absolute number, however, is only 14% compared to adults. The United States dominate global research by contributing the highest number of articles (777), Hirsch-Index (70), and citations (18,863). The most productive collaboration was between the United States and Canada (52). When considering nation's population and gross domestic product (GDP) rate, Norway is leading regarding population per article (62,467), per Hirsch-Index (223,841), per citation (2226), and per GDP (2.3E-04). Regarding publications per infant mortality rate, efforts of India and Brazil are remarkable. Out of the 100 most frequently publishing researchers, 25% were female. CONCLUSION: Research efforts on paediatric resuscitation have increased but remain underrepresented. Specifically, nations with high infant mortality rates should be integrated by collaborations. Additional efforts are required to overcome gender disparities.
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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 | 0.047 | 0.158 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.155 | 0.168 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".