- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
-
1756-0500
- Extent
-
Online-Ressource
- Language
-
Englisch
- Notes
-
online resource.
- Bibliographic citation
-
Predicting the success of induction of labour using cervical volume ; volume:14 ; number:1 ; day:18 ; month:12 ; year:2021 ; pages:1-3 ; date:12.2021
BMC Research Notes / Biomed Central; 14, Heft 1 (18.12.2021), 1-3, 12.2021
- Creator
-
Patabendige, Malitha
Athulathmudali, Sanka Rajesh
- Contributor
-
SpringerLink (Online service)
- DOI
-
10.1186/s13104-021-05865-5
- URN
-
urn:nbn:de:101:1-2022041319464604619290
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
15.08.2025, 7:28 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Patabendige, Malitha
- Athulathmudali, Sanka Rajesh
- SpringerLink (Online service)
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Comparative study Of TVS cervical score and Bishop score in prediction of successful labour induction
Derivation and validation of a model predicting the likelihood of vaginal birth following labour induction
Misoprostol (Cytotec) for induction of labour
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Comparison of various methods of induction of cervical ripening and labour, including prostaglandins, oxytocin, oestradiol and mechanical means
Predicting scientific success based on coauthorship networks
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Quantitative Elastography of the Cervix for Predicting Labor Induction Success
The Renaissance of Transcervical Balloon Catheters for Cervical Ripening and Labour Induction
Double- versus single-balloon catheters for labour induction and cervical ripening: a meta-analysis
Prediction of an effective cervical ripenning in the induction of labour using vaginal dinoprostone
Comparative study Of TVS cervical score and Bishop score in prediction of successful labour induction
Derivation and validation of a model predicting the likelihood of vaginal birth following labour induction
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Predicting Students’ Success Using Neural Networks
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