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Clinical algorithms for the monitoring and management of spontaneous, uncomplicated labour and childbirth.
- Source :
- BJOG: An International Journal of Obstetrics & Gynaecology; Aug2024 Supplement 1, Vol. 131 Issue 2, p17-27, 11p
- Publication Year :
- 2024
-
Abstract
- Aim: To develop evidence‐based clinical algorithms for the assessment and management of spontaneous, uncomplicated labour and vaginal birth. Population: Pregnant women at any stage of labour, with singleton, term pregnancies considered to be at low risk of developing complications. Setting: Health facilities in low‐ and middle‐income countries. Search Strategy: We searched for relevant published algorithms, guidelines, systematic reviews and primary research studies on Cochrane Library, PubMed, and Google on terms related to spontaneous, uncomplicated labour and childbirth up to 01 June 2023. Case scenarios: Three case scenarios were developed to cover assessments and management for spontaneous, uncomplicated first, second and third stage of labour. The algorithms provide pathways for definition, assessments, diagnosis, and links to other algorithms in this series for management of complications. Conclusions: We have developed three clinical algorithms to support evidence‐based decision making during spontaneous, uncomplicated labour and vaginal birth. These algorithms may help guide health care staff to institute respectful care, appropriate interventions where needed, and potentially reduce the unnecessary use of interventions during labour and childbirth. Evidence‐based clinical algorithms may help support respectful, high quality intrapartum care. [Correction added on 23 August 2024, after first online publication: The Abstract has been updated to structure layout in this version.] [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 14700328
- Volume :
- 131
- Issue :
- 2
- Database :
- Complementary Index
- Journal :
- BJOG: An International Journal of Obstetrics & Gynaecology
- Publication Type :
- Academic Journal
- Accession number :
- 179238256
- Full Text :
- https://doi.org/10.1111/1471-0528.17895