Literature DB >> 963175

Comparing the joint agreement of several raters with another rater.

G W Williams.   

Abstract

In laboratory proficiency surveys which use reference laboratories for the evaluation of participant laboratories, a measure of the agreement of the participant laboratory with the reference laboratories is needed which considers the extent of agreement (or disagreement) among the reference laboratories themselves. In, a measure of nominal scale agreement, is proposed. In is interpreted as follows: Let a specimen be selected at random and rated by a reference laboratory which itself has been randomly selected from the n reference laboratories. If the specimen was also rated by the participant laboratory, this second rating would agree with the first at a rate In of the rate that would be obtained by a second randomly selected reference laboratory. An approximate (large sample) confidence interval for the ratio In is developed. In order to account for the more general case of scaled agreement, a weighted index of agreement is also considered.

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Year:  1976        PMID: 963175

Source DB:  PubMed          Journal:  Biometrics        ISSN: 0006-341X            Impact factor:   2.571


  13 in total

1.  Validation of an automated system for luminal and medial-adventitial border detection in three-dimensional intravascular ultrasound.

Authors:  Jon D Klingensmith; E Murat Tuzcu; Steven E Nissen; D Geoffrey Vince
Journal:  Int J Cardiovasc Imaging       Date:  2003-04       Impact factor: 2.357

2.  A mutual information-based metric for evaluation of fMRI data-processing approaches.

Authors:  Babak Afshin-Pour; Hamid Soltanian-Zadeh; Gholam-Ali Hossein-Zadeh; Cheryl L Grady; Stephen C Strother
Journal:  Hum Brain Mapp       Date:  2011-05       Impact factor: 5.038

3.  Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms.

Authors:  Yimo Tao; Shih-Chung B Lo; Matthew T Freedman; Erini Makariou; Jianhua Xuan
Journal:  Med Phys       Date:  2010-11       Impact factor: 4.071

4.  Two methods for validating brain tissue classifiers.

Authors:  Marcos Martin-Fernandez; Sylvain Bouix; Lida Ungar; Robert W McCarley; Martha E Shenton
Journal:  Med Image Comput Comput Assist Interv       Date:  2005

5.  On evaluating brain tissue classifiers without a ground truth.

Authors:  Sylvain Bouix; Marcos Martin-Fernandez; Lida Ungar; Motoaki Nakamura; Min-Seong Koo; Robert W McCarley; Martha E Shenton
Journal:  Neuroimage       Date:  2007-04-25       Impact factor: 6.556

6.  Adaptive border marching algorithm: automatic lung segmentation on chest CT images.

Authors:  Jiantao Pu; Justus Roos; Chin A Yi; Sandy Napel; Geoffrey D Rubin; David S Paik
Journal:  Comput Med Imaging Graph       Date:  2008-06-02       Impact factor: 4.790

7.  Real time monitoring and analysis via the medical information bus, Part II.

Authors:  J A Taboada; B Arcay; J E Arias
Journal:  Med Biol Eng Comput       Date:  1997-09       Impact factor: 2.602

8.  Dealing with inter-expert variability in retinopathy of prematurity: A machine learning approach.

Authors:  V Bolón-Canedo; E Ataer-Cansizoglu; D Erdogmus; J Kalpathy-Cramer; O Fontenla-Romero; A Alonso-Betanzos; M F Chiang
Journal:  Comput Methods Programs Biomed       Date:  2015-06-16       Impact factor: 5.428

9.  Statistical framework for validation without ground truth of choroidal thickness changes detection.

Authors:  Tiziano Ronchetti; Christoph Jud; Peter M Maloca; Selim Orgül; Alina T Giger; Christoph Meier; Hendrik P N Scholl; Rachel Ka Man Chun; Quan Liu; Chi-Ho To; Boris Považay; Philippe C Cattin
Journal:  PLoS One       Date:  2019-06-28       Impact factor: 3.240

10.  High resolution multidetector CT-aided tissue analysis and quantification of lung fibrosis.

Authors:  Vanessa A Zavaletta; Brian J Bartholmai; Richard A Robb
Journal:  Acad Radiol       Date:  2007-07       Impact factor: 3.173

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