Objective: Rates of induction of labour (IOL) have dramatically risen in recent years. Currently no universally adopted classification system for IOLs exists. A comprehensive classification system would enable analyses of trends and benchmarking of practices. A previously proposed classification attempted to address this issue, but it did not appear to be fully comprehensive, as it did not account for IOL for preterm prelabour rupture of membranes. Furthermore, without subcategorization to reflect some common indications the utility of the classification system is limited. We have aimed to create a Modified Grenoble Classification that is inclusive of all IOL indications and then explored the feasibility of implementing this system at our institution, a regional hospital in Queensland, Australia.
Study Design: We conducted a retrospective audit using routinely collected information for all women undergoing IOL at our facility across a two-year period (n = 1663). Cases were classified into one of eight groups, with the IOL for maternal or fetal pathology group further subcategorised by some common indications. Percentage of total IOLs were calculated for each group.
Results: The new classification was simple to implement at our institution. The largest groups consisted of IOL for maternal or fetal indication (67.6 %), post-dates gestation (15.0 %), and term prelabour rupture of membranes (9.5 %).
Conclusions: To our knowledge, this is the first paper describing the implementation of a classification system for IOL that is inclusive of all indications. We suggest this is a utile tool to allow analysis of IOL practices.
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http://dx.doi.org/10.1016/j.ejogrb.2025.01.017 | DOI Listing |
J Clin Rheumatol
March 2025
Coordinación de Investigación en Salud, Instituto Mexicano del Seguro Social, Puebla, Mexico.
Introduction: Patients with polymyositis and dermatomyositis (PM/DM) are prone to multiple complications that may lead to increased mortality rates. Data about PM/DM mortality in Mexico are lacking.
Objective: The aim of this study was to assess mortality trends in PM/DM in Mexico across 2 decades (2000-2019), overall, by sex, age group, and geographic region.
JMIR Public Health Surveill
March 2025
Nivel - Netherlands Institute for Health Services Research, Otterstraat 118, Utrecht, 3513 CR, The Netherlands, 31 629034652.
Background: Syndromic surveillance systems are crucial for the monitoring of population health and the early detection of emerging health problems. Internationally, there are numerous established systems reporting on different types of data. In the Netherlands, the Nivel syndromic surveillance system provides real-time monitoring on all diseases and symptoms presented in general practice.
View Article and Find Full Text PDFPLoS One
March 2025
Department of Physiology and Biochemistry of Nutrition, Max Rubner-Institut, Karlsruhe, Germany.
Background: For a growing number of food-based dietary guidelines (FBDGs), diet optimization is the tool of choice to account for the complex demands of healthy and sustainable diets. However, decisions about such optimization models' parameters are rarely reported nor systematically studied.
Objectives: The objectives were to develop a framework for (i) the formulation of decision variables based on a hierarchical food classification system; (ii) the mathematical form of the objective function; and (iii) approaches to incorporate nutrient goals.
Background: In Germany, the incidence of traumatic spinal cord injury is approximately 16 per million inhabitants per year. This article aims to present evidence-based diagnostic and therapeutic measures for the first 14 days after injury to minimize neural damage, prevent complications, and preserve functioning as much as possible.
Methods: After the formulation of key questions, systematic literature searches were carried out on multiple topics.
Mol Inform
March 2025
Faculty of Information Technology, HUTECH University, Ho Chi Minh City, Vietnam.
Within a recent decade, graph neural network (GNN) has emerged as a powerful neural architecture for various graph-structured data modelling and task-driven representation learning problems. Recent studies have highlighted the remarkable capabilities of GNNs in handling complex graph representation learning tasks, achieving state-of-the-art results in node/graph classification, regression, and generation. However, most traditional GNN-based architectures like GCN and GraphSAGE still faced several challenges related to the capability of preserving the multi-scaled topological structures.
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