{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T07:46:32Z","timestamp":1791963992396,"version":"4.3.4"},"reference-count":31,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000061","name":"Fogarty International Center","doi-asserted-by":"publisher","award":["D43 TW009337"],"award-info":[{"award-number":["D43 TW009337"]}],"id":[{"id":"10.13039\/100000061","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000061","name":"Fogarty International Center","doi-asserted-by":"publisher","award":["D43\u2010TW012205"],"award-info":[{"award-number":["D43\u2010TW012205"]}],"id":[{"id":"10.13039\/100000061","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009633","name":"Eunice Kennedy Shriver National Institute of Child Health and Human Development","doi-asserted-by":"publisher","award":["K23 HD112548"],"award-info":[{"award-number":["K23 HD112548"]}],"id":[{"id":"10.13039\/100009633","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["World j. surg."],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Injuries are responsible for 950,000 deaths per year among children and adolescents under 18\u00a0years 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\u2010term mortality prediction in pediatric populations in low\u2010 and middle\u2010income countries (LMICs). Using variables available at the bedside in resource\u2010limited settings, PRESTO has been validated in South Africa, Rwanda, and Tanzania. In Tanzania, our team found these variables were present for most pediatric injury patients and that the model performed well in predicting mortality. However, we faced challenges due to small sample size and mortality being a rare outcome.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>We sought to determine whether we could improve predictability of the PRESTO model for pediatric patients by increasing sample size through inclusion of adult trauma patients from the same population at Kilimanjaro Christian Medical Center (KCMC) in Moshi, Tanzania.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Design\/Methods<\/jats:title>\n                    <jats:p>Data were collected between November 2020 and February 2024 from a pediatric registry, and between April 2018 and February 2024 from an adult injury registry at KCMC. Missing data were addressed using multiple imputation. The dataset was split into training and testing sets (75\/25). Ten machine learning algorithms were trained to predict in\u2010hospital mortality using clinical and demographic variables, with performance evaluated using cross\u2010validation, ROC\u2010AUC, sensitivity, and specificity.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 5635 injury patients (911 pediatric and 4724 adults) were included. Pediatric in\u2010hospital mortality was 6.8%, while adult mortality was 4.4%. Pediatric patients presented with worse GCS scores compared to adults (4% severe vs. 1.4% severe). The best\u2010performing models were Random Forest (RF), C5, and Extreme Gradient Boosting (XGB), with ROC\u2010AUCs of 0.92, 0.90, and 0.81. In the test set, ROC\u2010AUC for pediatric patients was lower than for adults, with differences of 0.08, 0.06, and 0.05 for XGB, RF, and C5, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Adding adult data did not improve predictability of the PRESTO model, possibly due to the fact that the lower mortality and injury severity in adults compared to children in our dataset statistically diluted the model. This supports the idea that children should be considered separately, especially for vital signs and mortality prediction. These findings highlight the challenge of predicting a rare outcome, and emphasize the need to increase pediatric registry sample sizes to develop more accurate models for mortality risk stratification in LMICs.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/wjs.70553","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T04:35:16Z","timestamp":1788496516000},"page":"3103-3110","source":"Crossref","is-referenced-by-count":0,"title":["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"],"prefix":"10.1002","volume":"50","author":[{"given":"Joao Vitor Perez","family":"de Souza","sequence":"first","affiliation":[{"name":"Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Department of Emergency Medicine Duke University School of Medicine  Durham North Carolina USA"},{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5263-6612","authenticated-orcid":false,"given":"Elizabeth M.","family":"Keating","sequence":"additional","affiliation":[{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Division of Pediatric Emergency Medicine University of Utah  Salt Lake City Utah USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"William","family":"Nkenguye","sequence":"additional","affiliation":[{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Kilimanjaro Clinical Research Institute  Moshi Tanzania"},{"name":"Department of Epidemiology and Applied Biostatistics KCMC University  Moshi Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4116-7189","authenticated-orcid":false,"given":"Rosalia","family":"Njau","sequence":"additional","affiliation":[{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Kilimanjaro Clinical Research Institute  Moshi Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8571-3699","authenticated-orcid":false,"given":"Happiness","family":"Kajoka","sequence":"additional","affiliation":[{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Kilimanjaro Clinical Research Institute  Moshi Tanzania"},{"name":"Department of Epidemiology and Applied Biostatistics KCMC University  Moshi Tanzania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2282-4751","authenticated-orcid":false,"given":"Pollyana Coelho Pessoa","family":"Santos","sequence":"additional","affiliation":[{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Pediatric Residency University of North Carolina UNC  Chapel Hill North Carolina USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2707-2720","authenticated-orcid":false,"given":"Edwin Joseph","family":"Shewiyo","sequence":"additional","affiliation":[{"name":"GEMINI Research Center Duke Global Health Institute Duke University  Durham North Carolina USA"},{"name":"Kilimanjaro Clinical Research Institute  Moshi Tanzania"},{"name":"Department of Epidemiology and Applied Biostatistics KCMC University  Moshi 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