Decisions for patient transport by emergency medical services (EMS) are individualized; while established guidelines help direct adult patients to specialty hospitals, no such pediatric equivalents are in wide use. When children are transported to a hospital that cannot provide definitive care, care is delayed and may cause adverse events. Therefore, we created a novel evidence-based decision tool to support EMS destination choice. A multidisciplinary expert panel (EP) of stakeholders reviewed published literature. Four facility capability levels for pediatric care were defined. Using a modified Delphi method, the EP matched specific conditions to a facility pediatric-capability level in a draft tool. The literature review and EP recommendations identified seventeen pediatric medical conditions at risk for secondary transport. In the first voting round, two were rejected, nine met consensus for a specific facility capability level, and six did not reach consensus on the destination facility level. A second round reached consensus on a facility level for the six conditions as well as revision of one previously rejected condition. In the third round, the panel selected a visual display format. Finally, the panel unanimously approved the PDTree. Using a modified Delphi technique, we developed the PDTree EMS destination decision tool by incorporating existing evidence and the expertise of a multidisciplinary panel.
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http://dx.doi.org/10.3390/children8080658 | DOI Listing |
BMC Med Inform Decis Mak
January 2025
Department of Orthopedics, the First Hospital of Jilin University, Changchun, Jilin Province, 130021, China.
Purpose: Identifying patients who may benefit from multiple drilling are crucial. Hence, the purpose of the study is to utilize radiomics and deep learning for predicting no-collapse survival in patients with femoral head osteonecrosis.
Methods: Patients who underwent multiple drilling were enrolled.
Neurosurg Rev
January 2025
Department of Neurosurgery, Beijing Friendship hospital, Capital Medical University, No. 95 Yong 'an Road, Xicheng District, Beijing, China.
Patients with cerebral venous thrombosis (CVT) may experience poor response to anticoagulant therapy and delayed surgical treatment may lead to clinical deterioration. However, the factors contributing to clinical deterioration remain poorly understood. Patients with CVT from three centers between January 2017 and October 2023 were included and grouped as the development cohort and validation cohort.
View Article and Find Full Text PDFUpdates Surg
January 2025
Alluri Sitarama Raju Academy of Medical Sciences, Eluru, India.
There is a growing importance for patients to easily access information regarding their medical conditions to improve their understanding and participation in health care decisions. Artificial Intelligence (AI) has proven as a fast, efficient, and effective tool in educating patients regarding their health care conditions. The aim of the study is to compare the responses provided by AI tools, ChatGPT and Google Gemini, to assess for conciseness and understandability of information provided for the medical conditions Deep vein thrombosis, decubitus ulcers, and hemorrhoids.
View Article and Find Full Text PDFAbdom Radiol (NY)
January 2025
Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
Purpose: Mesenteric artery embolism (MAE) is a relatively uncommon abdominal surgical emergency, but it can lead to catastrophic clinical outcomes if the diagnosis is delayed. This study aims to build a prediction model of clinical-radiomics nomogram for early diagnosis of MAE based on non-contrast computed tomography (CT) and biomarkers.
Method: In this retrospective study, a total of 364 patients confirmed as MAE (n = 131) or non-MAE (n = 233) who were randomly divided into a training cohort (70%) and a validation cohort (30%).
Sci Rep
January 2025
Department of Radiology, Affiliated Hospital of North Sichuan Medical College, No.63 Wenhua Road, Shunqing District, Nanchong, 637000, China.
This study sought to establish and validate an interpretable CT radiomics-based machine learning model capable of predicting post-acute pancreatitis diabetes mellitus (PPDM-A), providing clinicians with an effective predictive tool to aid patient management in a timely fashion. Clinical and imaging data from 271 patients who had undergone enhanced CT scans after first-episode acute pancreatitis from March 2017-June 2023 were retrospectively analyzed. Patients were classified into PPDM-A (n = 109) and non-PPDM-A groups (n = 162), and split into training (n = 189) and testing (n = 82) cohorts at a 7:3 ratio.
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