Prediction models for determining the success of labour induction: A systematic review and critical analysis.

D'Souza, Rohan; Ashraf, Rizwana; Foroutan, Farid

D'Souza, Rohan; Ashraf, Rizwana; Foroutan, Farid.

Afiliação

D'Souza R; Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada; Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; Division of Maternal and Fetal Medicine, Department of Obstetrics & Gynaecology, Mount Sinai Hospital, University of Toronto, Toronto, Canada. Electronic address: rohan@mcmaster.ca.
Ashraf R; Department of Obstetrics and Gynecology, McMaster University, Hamilton, Ontario, Canada.
Foroutan F; Health Research Methods, Evidence and Impact, McMaster University, Hamilton, Ontario, Canada; Ted Rogers Centre for Heart Research, Peter Munk Cardiac Center, University Health Network, Toronto, ON, Canada.

Best Pract Res Clin Obstet Gynaecol ; 79: 42-54, 2022 Mar.

Article em En | MEDLINE | ID: mdl-35090827

Assuntos

Trabalho de Parto Induzido; Feminino; Humanos; Trabalho de Parto Induzido/métodos; Gravidez

Palavras-chave

Caesarean; Induction of labour; Prediction model; Systematic review; Vaginal birth

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Trabalho de Parto Induzido Tipo de estudo: Guideline / Prognostic_studies / Risk_factors_studies / Systematic_reviews Limite: Female / Humans / Pregnancy Idioma: En Revista: Best Pract Res Clin Obstet Gynaecol Ano de publicação: 2022 Tipo de documento: Article

Texto completo

Imprimir

XML

PubMed Links

Buscar no Google