Global Trends in Neonatal Sepsis: A Scopus Bibliometric Analysis of Publications from 2015 to 2025
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.115 | 0.030 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it