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Analysis of obstetricians' decision making on CTG recordings
J. Spilka, V. Chudáček, P. Janků, L. Hruban, M. Burša, M. Huptych, L. Zach, L. Lhotská,
Jazyk angličtina Země Spojené státy americké
Typ dokumentu časopisecké články, práce podpořená grantem
Grantová podpora
NT11124
MZ0
CEP - Centrální evidence projektů
Digitální knihovna NLK
Plný text - Článek
Zdroj
NLK
Free Medical Journals
od 2001 do Před 1 rokem
Elsevier Open Access Journals
od 2001-02-01 do Před 1 rokem
Elsevier Open Archive Journals
od 2001-02-01 do Před 1 rokem
- MeSH
- kardiotokografie statistika a číselné údaje MeSH
- lidé MeSH
- metody pro podporu rozhodování * MeSH
- odchylka pozorovatele MeSH
- porodnictví statistika a číselné údaje MeSH
- reprodukovatelnost výsledků MeSH
- rozpoznávání automatizované metody MeSH
- senzitivita a specificita MeSH
- systémy pro podporu klinického rozhodování * MeSH
- umělá inteligence * MeSH
- Check Tag
- lidé MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
Interpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed clinicians' agreement on CTG evaluation based on quantitative measures (e.g. kappa coefficient) that do not offer any insight into clinical decision making. In this paper we aim to examine the agreement on evaluation in detail and provide data-driven analysis of clinical evaluation. For this study, nine obstetricians provided clinical evaluation of 634 CTG recordings (each ca. 60min long). We studied the agreement on evaluation and its dependence on the increasing number of clinicians involved in the final decision. We showed that despite of large number of clinicians the agreement on CTG evaluations is difficult to reach. The main reason is inherent inter- and intra-observer variability of CTG evaluation. Latent class model provides better and more natural way to aggregate the CTG evaluation than the majority voting especially for larger number of clinicians. Significant improvement was reached in particular for the pathological evaluation - giving a new insight into the process of CTG evaluation. Further, the analysis of latent class model revealed that clinicians unconsciously use four classes when evaluating CTG recordings, despite the fact that the clinical evaluation was based on FIGO guidelines where three classes are defined.
Citace poskytuje Crossref.org
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- $a Spilka, Jiří $u Department of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic. Electronic address: spilka.jiri@fel.cvut.cz.
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- $a Interpretation of cardiotocogram (CTG) is a difficult task since its evaluation is complicated by a great inter- and intra-individual variability. Previous studies have predominantly analyzed clinicians' agreement on CTG evaluation based on quantitative measures (e.g. kappa coefficient) that do not offer any insight into clinical decision making. In this paper we aim to examine the agreement on evaluation in detail and provide data-driven analysis of clinical evaluation. For this study, nine obstetricians provided clinical evaluation of 634 CTG recordings (each ca. 60min long). We studied the agreement on evaluation and its dependence on the increasing number of clinicians involved in the final decision. We showed that despite of large number of clinicians the agreement on CTG evaluations is difficult to reach. The main reason is inherent inter- and intra-observer variability of CTG evaluation. Latent class model provides better and more natural way to aggregate the CTG evaluation than the majority voting especially for larger number of clinicians. Significant improvement was reached in particular for the pathological evaluation - giving a new insight into the process of CTG evaluation. Further, the analysis of latent class model revealed that clinicians unconsciously use four classes when evaluating CTG recordings, despite the fact that the clinical evaluation was based on FIGO guidelines where three classes are defined.
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