Samuel R Neal
Biographic Data
| ID | 9076891 |
|---|---|
| NAME | Samuel R Neal |
| GIVEN NAMES | Samuel R |
| FAMILY NAME | Neal |
| SIGNATURE | NEAL S R |
| AFFILIATIONS | University College London |
| ORCID | 0000-0001-6832-9839 |
| VERIFIED | Yes |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries
INTRODUCTION: Neonatal sepsis causes significant morbidity and mortality worldwide but is difficult to diagnose clinically. Clinical prediction models (CPMs) could improve diagnostic accuracy, facilitating earlier treatment for cases and avoiding antibiotic overuse. Neonates in low-income and middle-income countries (LMICs) are disproportionately affected by sepsis, yet no review has comprehensively synthesised evidence for CPMs validated in this…
Risk factors of mortality in neonates with neonatal encephalopathy in a tertiary newborn care unit in Zimbabwe over a 12-month period
Neonatal encephalopathy (NE) accounts for ~23% of the 2.4 million annual global neonatal deaths. Approximately 99% of global neonatal deaths occur in low-resource settings, however, accurate data from these low-resource settings are scarce. We reviewed risk factors of neonatal mortality in neonates admitted with neonatal encephalopathy from a tertiary neonatal unit in Zimbabwe. A retrospective review of risk factors of short-term neonatal encepha…
No prominent works on this page.
Risk factors of mortality in neonates with neonatal encephalopathy in a tertiary newborn care unit in Zimbabwe over a 12-month period
Neonatal encephalopathy (NE) accounts for ~23% of the 2.4 million annual global neonatal deaths. Approximately 99% of global neonatal deaths occur in low-resource settings, however, accurate data from these low-resource settings are scarce. We reviewed risk factors of neonatal mortality in neonates admitted with neonatal encephalopathy from a tertiary neonatal unit in Zimbabwe. A retrospective review of risk factors of short-term neonatal encepha…
Clinical prediction models to diagnose neonatal sepsis in low-income and middle-income countries
INTRODUCTION: Neonatal sepsis causes significant morbidity and mortality worldwide but is difficult to diagnose clinically. Clinical prediction models (CPMs) could improve diagnostic accuracy, facilitating earlier treatment for cases and avoiding antibiotic overuse. Neonates in low-income and middle-income countries (LMICs) are disproportionately affected by sepsis, yet no review has comprehensively synthesised evidence for CPMs validated in this…
Internal Medicine (2 works) · Medicine (2 works) · Neonatal Respiratory Health Research (2 works) · Neonatal sepsis (2 works) · Pediatrics (2 works) · Sepsis (2 works) · Caesarean section (1 work) · Case fatality rate (1 work) · Cochrane Library (1 work) · Confidence interval (1 work)