Evaluation of COPD Longitudinally to Identify Predictive Surrogate End-points (ECLIPSE)

By Jorgen Vestbo, W. Anderson, H. Coxson, C. Crim, F. Dawber, L. Edwards, G. Hagan, K. Knobil, David A. Lomas, W. Macnee and 3 more. European Respiratory Journal, 2008

787 citations recorded by the source corpus.

Subfields: Physiology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/3efd8c0d7f0560b6362e086f84363921fda1e5b9

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Physiology

1. W. Macnee
2. P. Calverley
3. D. Parums

In Pulmonary and Respiratory Medicine

1. D. Parums
2. P. Calverley
3. Jorgen Vestbo
4. Alvar Agusti
5. W. Macnee
6. Bartolome Celli

Citations in the corpus

• The present and future of deep learning in radiology. (2019, background): “The dataset consisted of 7,983 COPD Gene participants for training and 1000 COPDGene [77] and 1672 ECLIPSE (Evaluations of COPD Longitudinally to Identify Predictive Surrogate Endpoints) [78] participants for testing.”
• Persistent Systemic Inflammation is Associated with Poor Clinical Outcomes in COPD: A Novel Phenotype (2012, methodology): “gov identifier NCT00292552; GSK study code SCO104960) have been published previously [17].”
• Characterisation of COPD heterogeneity in the ECLIPSE cohort (2010, methodology): “Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) is a large observational study of COPD patients and controls conducted at 46 centres in 12 countries aimed at defining COPD phenotypes and identifying biomarkers and/or genetic parameters that help to predict dise”