Health AI Central
Peer-Reviewed Publication
World J Surg2026September 3, 2026Journal Article

The Challenges of Predicting Rare Outcomes: A Critical Appraisal of Machine Learning Using the Pediatric Resuscitation and Trauma Outcome (PRESTO) Model in a Tanzanian Injury Registry.

Joao Vitor Pereze de Souza1,2,3, Elizabeth M Keating3,4, William Nkenguye3,5,6, Rosalia Njau3,5, Happiness Kajoka3,5,6, Pollyana Coelho Pessoa Santos3,7, Edwin Joseph Shewiyo3,5,6, Catherine A Staton1,2,3, Blandina T Mmbaga1,3,5,8, Joao Ricardo Nickenig Vissoci1,2,3
1Duke Global Health Institute, Duke University, Durham, North Carolina, USA.
2Department of Emergency Medicine, Duke University School of Medicine, Durham, North Carolina, USA.
3GEMINI Research Center, Duke Global Health Institute, Duke University, Durham, North Carolina, USA.
4Division of Pediatric Emergency Medicine, University of Utah, Salt Lake City, Utah, USA.
5Kilimanjaro Clinical Research Institute, Moshi, Tanzania.
6Department of Epidemiology and Applied Biostatistics, KCMC University, Moshi, Tanzania.
7Pediatric Residency, University of North Carolina, UNC, Chapel Hill, North Carolina, USA.
8Department of Paediatric and Child Health, KCMC University, Moshi, Tanzania.
PubMed DOI

Abstract

BACKGROUND: Injuries are responsible for 950,000 deaths per year among children and adolescents under 18 years old. Trauma prediction scores are useful in determining severity and prognosis of injury patients. The pediatric resuscitation and trauma outcome (PRESTO) score was developed as a simple score for short-term mortality prediction in pediatric populations in low- and middle-income countries…

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