Electrothermal Vessel Sealing Versus Conventional Suturing in Abdominal Hysterectomy: A Randomised Trial.

INTRODUCTION: The present randomised controlled trial was conducted to compare haemostatic efficiency, operative time, and overall performance of the electrothermal bipolar vessel sealing (EBVS) system with conventional suturing in abdominal hysterectomy. MATERIALS AND METHODS: The trial was designed with standard parallel arms, i.e., vessel sealing and suture ligature arms. Sixty patients were block randomised into either arms with 30 patients in each. A hand-held vessel sealing instrument was used to perform a hysterectomy in the vessel sealing arm and the quality of the uterine artery seal achieved at the first attempt was graded on an ordinal scale of 1-3 to quantify haemostatic efficiency. Operative time, intra-operative blood loss, and peri-operative complications were compared between the two arms. RESULTS: Significantly reduced mean operative time (26.97±8.92 vs 33.67±8.62 minutes; p=0.005) and intra-operative blood loss (111±53.31 mL vs 320±193.90 mL; p=0.001) was observed in the Vessel Sealing Arm compared to Suture Ligature Arm. Of total 60 uterine seals (from bilateral uterine artery transaction in 30 hysterectomies in the Vessel Sealing Arm), 83.34% were Level 1 with Complete Seal and no residual bleeding, 8.33% were Level 2 or Partial Seals with minimal bleeding, requiring the use of vessel sealers for a second time, while 8.33% had Seal Failure (Level 3) with significant bleeding requiring additional re-security of stumps with sutures. Modal pain scores on the first three postoperative days and duration of hospital stay were significantly less in the Vessel Sealer Arm, reflecting reduced postoperative morbidity. Outcomes were comparable across operators. CONCLUSION: Vessel Sealing System gives superior surgical results with lesser operative time, minimal blood loss, and reduced morbidity.

Automated credit assessment framework using ETL process and machine learning.

In the current business scenario, real-time analysis of enterprise data through Business Intelligence (BI) is crucial for supporting operational activities and taking any strategic decision. The automated ETL (extraction, transformation, and load) process ensures data ingestion into the data warehouse in near real-time, and insights are generated through the BI process based on real-time data. In this paper, we have concentrated on automated credit risk assessment in the financial domain based on the machine learning approach. The machine learning-based classification techniques can furnish a self-regulating process to categorize data. Establishing an automated credit decision-making system helps the lending institution to manage the risks, increase operational efficiency and comply with regulators. In this paper, an empirical approach is taken for credit risk assessment using logistic regression and neural network classification method in compliance with Basel II standards. Here, Basel II standards are adopted to calculate the expected loss. The required data integration for building machine learning models is done through an automated ETL process. We have concluded this research work by evaluating this new methodology for credit risk assessment.