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The Robson Classification System is recognised as a first step for optimising the use of caesarean section and as a strategy for continuous quality improvement in maternal and newborn health. This Viewpoint provides a detailed account of the strategy adopted and lessons learned from a collaborative initiative to institutionalise the Robson Classification into Pakistan's health system. We developed a training package which emphasised capacity building of senior clinicians to act as master trainers. We also developed a mobile application for data collection and analysis. Training workshops took place in 2020 in a selection of public sector, tertiary-level, teaching hospitals from across the country and data was collected on all births in participating hospitals' obstetric units for a full year. Pakistan is poised for scale-up with the Robson Classification embedded in 57% of Pakistan's public, tertiary, teaching hospitals. A core group of master trainers is positioned in every province, and a robust dataset is available. However, integration into any health system cannot be thought of as a finite project. It requires government commitment, training and an ongoing process with built-in data quality assurance and feedback to clinicians.
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http://dx.doi.org/10.1016/j.lansea.2024.100479 | DOI Listing |
Healthcare (Basel)
August 2025
Research Center for Higher Education, Tokushima University, Tokushima 770-8502, Japan.
Cesarean section (CS) is a critical surgical procedure in obstetrics but is increasingly overused worldwide. Vietnam has seen rising CS rates, especially in urban tertiary hospitals, with limited standardized analysis to guide interventions. This study assesses CS rates at Tu Du Hospital using the WHO-endorsed Robson 10-Group Classification System.
View Article and Find Full Text PDFRecenti Prog Med
September 2025
Department of Clinical and Experimental Medicine, Magna Graecia University of Catanzaro, Italy.
Background: The global increase in caesarean section (CS) rates raises concerns about maternal and neonatal outcomes. Italy, with one of the highest CS rates in Europe, especially in the Calabria Region, faces challenges in reducing this trend. The Calabria Region has joined the "Easy-Net" Network Program (NET-2016-02364191) for the evaluation of audit & feedback (A&F) interventions to reduce CS rate.
View Article and Find Full Text PDFComput Biol Med
August 2025
Secretaría de Salud de la Ciudad de Mexico, Cuauhtémoc, C.P. 06900, Mexico City, Mexico. Electronic address:
We present a manually annotated dataset focused on Robson Criteria Classification, encompassing entities related to obstetric variables, antibiotics, uterotonics, complications, delivery outcomes, and personal information. A total of 1,865 Electronic Health Records (EHRs) were annotated, yielding 18,105 labeled entities. Inter-annotator agreement (IAA) was assessed using F1 and Kappa scores.
View Article and Find Full Text PDFJ Imaging
August 2025
Obstetrics and Gynaecology Unit, Department of Biomedical Sciences and Human Oncology, University of Bari "Aldo Moro", 70124 Bari, Italy.
Global cesarean section (CS) rates continue to rise, with the Robson classification widely used for analysis. However, Robson Group 2A patients (nulliparous women with induced labor) show disproportionately high CS rates that cannot be fully explained by demographic factors alone. This study explored how the Artificial Intelligence Dystocia Algorithm (AIDA) could enhance the Robson system by providing detailed information on geometric dystocia, thereby facilitating better understanding of factors contributing to CS and developing more targeted reduction strategies.
View Article and Find Full Text PDFCad Saude Publica
August 2025
Universidade Federal do Maranhão, São Luís, Brasil.
Temporal trends in cesarean section (C-sections) rates were analyzed in Brazil and its regions using the Robson classification system. An ecological time-series study was conducted with data from the Brazilian Ministry of Health about C-section rates from 2014 to 2022. Joinpoint models were used to estimate percentage changes in C-section rate trends in the units of analysis.
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