An audit of caesarean section rate based on Robson’s ten group classification system

Background: Caesarean section (CS) rates are rising worldwide and is a major public health concern. There is lack of evidence supporting the maternal and neonatal benefits with the increasing CS rates. Robson’s ten group classification system serves as an initial structure with which caesarean section rates can be analysed. RTGCS helps us to analyse and allow us to bring changes in our practice.Methods: This was a hospital based cross sectional study conducted over a period of 10 months during the year 2018, which involved 1478 pregnant women, out of which 693 underwent CS, those who underwent CS were grouped according to Robson’s Ten group classification system and the data was collected and analyzed.Results: 693 women underwent CS and the overall section rate was 46.88%. Group 5 (previous LSCS) and Group 2 (nulliparous, >37 weeks, induced) contributed the maximum to the overall CS rates (33.9% and 26.3% respectively). The most common indication for caesarean section was previous LSCS (38%), fetal distress (19.2%) and meconium stained liquor (13.7%).Conclusions: Robson’s ten group classification system helps us in auditing the caesarean section rates. Group 5 and 2 contributes the maximum for caesarean section rates. Encouraging and adequate counselling for VBAC, proper training of obstetricians in CTG interpretation would reduce the caesarean section rates

An audit of caesarean section rate based on Robson’s ten group classification system

Background: Caesarean section (CS) rates are rising worldwide and is a major public health concern. There is lack of evidence supporting the maternal and neonatal benefits with the increasing CS rates. Robson’s ten group classification system serves as an initial structure with which caesarean section rates can be analysed. RTGCS helps us to analyse and allow us to bring changes in our practice.Methods: This was a hospital based cross sectional study conducted over a period of 10 months during the year 2018, which involved 1478 pregnant women, out of which 693 underwent CS, those who underwent CS were grouped according to Robson’s Ten group classification system and the data was collected and analyzed.Results: 693 women underwent CS and the overall section rate was 46.88%. Group 5 (previous LSCS) and Group 2 (nulliparous, &gt;37 weeks, induced) contributed the maximum to the overall CS rates (33.9% and 26.3% respectively). The most common indication for caesarean section was previous LSCS (38%), fetal distress (19.2%) and meconium stained liquor (13.7%).Conclusions: Robson’s ten group classification system helps us in auditing the caesarean section rates. Group 5 and 2 contributes the maximum for caesarean section rates. Encouraging and adequate counselling for VBAC, proper training of obstetricians in CTG interpretation would reduce the caesarean section rates.</jats:p

Hand Gesture Controlled Presentation Viewer with AI Virtual Painter

Online teaching has been encouraged for many years but the COVID-19 pandemic has promoted it to an even greater extent. Teachers had to quickly shift to online teaching methods and processes and conduct all the classroom activities online. The global pandemic has accelerated the transition from chalk and board learning to mouse and click - digital learning. Even though there are online whiteboards available for teaching, teachers often find it difficult to draw using a mouse. A solution for this would be to get an external digital board and stylus but not everyone would be able to afford it. The Hand-Gesture Controlled Presentation Viewer With AI Virtual Painter is a project where one can navigate through the presentation slides and draw anything on them just like how one would on a normal board, just by using their fingers. This project aims to digitalise the traditional blackboard-chalk system and eliminate the need for using a mouse or keyboard while taking classes. HandGesture controlled devices especially laptops and computers have recently gained a lot of attraction. This system works by detecting landmarks on one’s hand to recognise the gestures. The project recognises five hand gestures. It uses a thumb finger for moving to the next slide, a little finger for moving to the previous slide, two fingers for displaying the pointer, one finger for drawing on the screen and three fingers for erasing whatever has been drawn. The infrastructure is provided between the user and the system using only a camera. The camera’s output will be presented on the system’s screen so that the user can further calibrate it. Keywords: Hand Gestures, Gesture Detection, Virtual Painter.</jats:p