Global Trends in Neonatal Sepsis: A Scopus Bibliometric Analysis of Publications from 2015 to 2025
Bibliographic record
Abstract
Neonatal sepsis remains a significant global health challenge, contributing to substantial morbidity and mortality, particularly in low- and middle-income countries (LMICs). This bibliometric study aimed to analyze research trends, key contributors, emerging themes, and collaborative networks in the neonatal sepsis literature from 2015 to mid-2025. The Scopus database was searched using relevant keywords. After applying the inclusion and exclusion criteria, the final dataset was analyzed using bibliometric methods. The annual publication trend showed a steady growth from 2015 to 2020. The United States, China, and India were the top contributors, while the University of Toronto, St. George’s University of London, and Inserm are leading institutions. Keyword co-occurrence analysis revealed clusters around biomarkers, maternal health, and antimicrobial resistance. Collaboration networks highlighted strong partnerships among high-income countries but limited integration with high-burden regions. Key research gaps include the need for context-specific diagnostic tools, capacity building in LMICs, and understanding the long-term outcomes of neonatal sepsis survivors. This study emphasizes the urgent need for equitable research investments, strengthened global partnerships, and targeted interventions to reduce the burden of neonatal sepsis, particularly in regions with the highest disease burden.
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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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.135 | 0.252 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".