− HEALTH Background Injuries are responsible for 950,000 deaths per year among children and adolescents under 18 years…− HEALTH Many problems in biomedicine can be posed as binary classification. When they are addressed using artificial…+ HEALTH Purpose The purpose of this study was to evaluate the performance of two general-use artificial intelligence…− HEALTH Early gastric cancer (EGC) is increasingly managed by endoscopic resection (ER); however, lymph node metastas…− LABOR Traditional secondary meta-analysis workflows are highly labour-intensive, time-consuming, and difficult to u…− HEALTH There is a growing literature on the prediction of risk of deterioration in hospital settings, including by l…− SCIENCE Self-driving cars increasingly rely on deep neural networks to achieve human-like driving 1-3 . The opacity o…+ HEALTH Objective This study aimed to develop a mitochondrial and hematopoiesis-related differentially expressed gene…
TruaceTracing the truth around AISaturday, September 5, 2026
Subscribe
TRV-2026-0987Certified recordPeer-reviewed

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

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 (LMICs). Using variables available at the bedside in resource-limited settings, PRESTO has been validated in South Af…

Health · P Space — documented harm · certified 2026-09-05 · v1 · article view · machine-readable

Current reading — problem

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: 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.

Evidence

Reader signal

How should this claim be treated?

Cite this record

Truvace Impact Record TRV-2026-0987, v1: “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.” Truvace, 2026-09-05. /record/TRV-2026-0987 (accessed at citation time). sha256 24cede7c0e9fd5ec…

Calibration history

Every change to this record since certification, in the open. None yet — the reading has held since it entered the record.

  1. Certifiedv124cede7c0e9f…

    Certified into the record

Verify this record
How to verify without trusting this page

Fetch the canonical text of any version from /api/record/TRV-2026-0987 and hash it yourself — for example shasum -a 256 on the saved canonical field. The result must equal content_hash, and each version’s text ends with prev:followed by the prior version’s hash (version 1 chains to 64 zeros). If a single character of any version had been altered since certification, the chain would not reproduce.