Back to search

Article

Concordance Between Dispatch Suspicion, On‑Scene Phenotype, and Time Sensitive Triage in Prehospital Infectious Presentations: A Retrospective Machine‑Learning Study

2026-01-28

Abstract excerpt

<title>Abstract</title> <p> <bold>Background:</bold> Early risk stratification of infectious presentations during emergency calls is challenging; symptom-based dispatch frequently diverges from physiological severity observed on scene. The concordance between the Emergency Medical Call Centres (EMCC) presenting problem (“Fever/Infection”), the first on-scene Emergency Symptoms and Signs (ESS) label, and high-ri...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work
6b19924a-6013-5d33-a099-0ca0542fb2ac
DOI
10.21203/rs.3.rs-7651316/v1
Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest
Interactive article relationship graphSelect a related publication card to move it into the centre and load its closest explainable connections. Solid lines are source-backed structured connections. Dashed lines are semantic discovery signals and are not scientific evidence.
Concordance Between Dispatch Suspicion, On‑Scene Phenotype, and Time Sensitive Triage in Prehospital Infectious Presentations: A Retrospective Machine‑Learning StudyDOI 10.21203/rs.3.rs-7651316/v1
A validation of machine learning-based risk scores in the prehospital settingSemantic proximity
Development and External Validation of a High-Precision Model for Predicting ICU Admission from Emergency Department TriageSemantic proximity
Machine learning for predicting clinical outcomes in emergency department patients with acute respiratory infections: A scoping reviewSemantic proximity
Artificial intelligence driven assessment of routinely collected healthcare data is an effective screening test for COVID-19 in patients presenting to hospitalSemantic proximity
Machine Learning-Based COVID-19 Patients Triage Algorithm Using Patient-Generated Health Data From Nationwide Multicenter DatabaseSemantic proximity
Rapid assessment of COVID-19 mortality risk with GASS classifiersSemantic proximity
Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutionsSemantic proximity
Multicenter analysis of COVID-19 hospitalizations and stacking machine learning algorithms for prediction of high-risk patientsSemantic proximity
Development and multicenter external validation of A Data-Driven Scoring System for Early and Rapid Identification of Sepsis in Emergency DepartmentsSemantic proximity
An external validation study of the Score for Emergency Risk Prediction (SERP), an interpretable machine learning-based triage score for the emergency departmentSemantic proximity
Emergency calls are early indicators of ICU bed requirement during the COVID-19 epidemic A retrospective study in Ile-de-France region, FranceSemantic proximity
A Tri-light Warning System for Hospitalized COVID-19 Patients: Credibility-based Risk Stratification under Data ShiftSemantic proximity
Select a neighboring publication to make it the new centre.