Variation among spirometry interpretation algorithms: Clinical implications Source: Annual Congress 2010 - From diagnosis to disease management in primary care Year: 2010
Explaining predictions of an automated pulmonary function test interpretation algorithm Source: International Congress 2019 – M-health/e-health I Year: 2019
Standardization of parameters, theoreticians, interpretation and reports in spirometry Source: Eur Respir J 2005; 26: Suppl. 49, 178s Year: 2005
Comparison of observer variability and accuracy of different criteria for lung scan interpretation Source: Eur Respir J 2001; 18: Suppl. 33, 226s Year: 2001
Comparison of biological and nonbiological quality control methods for assessing interlaboratory differences in TLCO and VA measurements Source: Eur Respir J 2003; 22: Suppl. 45, 145s Year: 2003
FeNO interpretation aid: A clinical decision support tool for interpretation of FeNO values in the patients with respiratory symptoms Source: Annual Congress 2011 - E-learning Year: 2011
Physician‘s mistakes in the interpretation of spirometry Source: Annual Congress 2012 - Going with the flow: assessment and evaluation of airway function and its role in patient management Year: 2012
Current clinical methods of measurement of respiratory rate give imprecise values Source: ERJ Open Res, 6 (3) 00023-2020; 10.1183/23120541.00023-2020 Year: 2020
The limitations of quantitative measures: assessing approaches to research into NIV use in MND Source: Annual Congress 2009 - New trends in noninvasive ventilation for chronic respiratory failure Year: 2009
Significant inconsistencies in the agreement of children’s respiratory rate measurements between different observers Source: International Congress 2017 – Assessing the impact of respiratory and sleep problems in children Year: 2017
There is no rationale to still rely on outdated, biased tools for quantitative morphology in pulmonary research Source: Eur Respir Rev 2006; 15: 105-106 Year: 2006
Inter-laboratory variability in the measurement of lung function - contribution of volume calibration errors Source: Eur Respir J 2004; 24: Suppl. 48, 183s Year: 2004
Quantification of breathing pattern variability on simulated and clinical data Source: Eur Respir J 2002; 20: Suppl. 38, 85s Year: 2002
The nose as a research tool: Intra-subject variability in nasal sampling Source: Annual Congress 2011 - Diagnosis and treatment of inflammatory respiratory diseases Year: 2011
Effects of physiological variation on parameter estimates in a dynamic model of FeNO Source: International Congress 2018 – Clinical determinants of asthma and biomarkers Year: 2018
Clustering of adherence variability metrics and clinical outcomes in asthma Source: International Congress 2018 – Clinical determinants of asthma and biomarkers Year: 2018
Interlaboratory variability in the measurement of lung function: contribution of volume calibration errors Source: Eur Respir J 2003; 22: Suppl. 45, 144s Year: 2003
The impact of different reference standards when estimating the accuracy of tests for diagnosing asthma Source: International Congress 2018 – Primary care management of asthma Year: 2018
When is FEV1 loss excessive? – an investigation of the relationship between year-to-year and long-term spirometry changes Source: Annual Congress 2004 - Longitudinal studies and determinants of lung function and COPD Year: 2004
Is there a need for ongoing support for the interpretation of spirometry results by general practitioners? Source: Eur Respir J 2006; 28: Suppl. 50, 740s Year: 2006