How I Do It: A Collaborative, Interinstitutional Program to Improve the Care of Patients with Chronic Thromboembolic Pulmonary Hypertension (CTEPH)
CHEST Pulmonary(2024)
From the Division of Pulmonary and Critical Care Medicine
Abstract
Chronic thromboembolic pulmonary hypertension (CTEPH), a subcategory of pulmonary hypertension (PH) and chronic sequela of acute pulmonary embolism (PE), is often underdiagnosed due to nonspecific symptoms. Pulmonary endarterectomy (PEA) remains the optimal, potentially curative therapy; however, determination of operability is based on multiple factors that may be relatively unique to each patient and largely based on physician expertise. Patients with CTEPH should be referred to CTEPH centers for comprehensive confirmatory diagnostics and operability assessments by multidisciplinary teams. As CTEPH center experience and expertise are key to improving clinical outcomes, challenges arise when they are not available or easily accessible to patients. This article describes the Michigan Multi-center CTEPH Collaboration, a multidisciplinary, interinstitutional collaboration program developed by healthcare centers in Michigan, USA. PH and PE teams from centers across the state share expertise and clinical opinions in a monthly virtual conference. This collaborative approach tackles the two main challenges in the treatment of CTEPH head on: accurate diagnosis and patient access to CTEPH centers. Our collaboration model can be used as a best practice across the nation to benefit patients, multidisciplinary teams, and CTEPH centers.
MoreTranslated text
Key words
chronic thromboembolic pulmonary hypertension,CTEPH,multidisciplinary team model,pulmonary hypertension
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined