ABSTRACT
Hemorrhoidectomy is one of the most common surgical interventions to remove the third and fourth degrees of prolapse hemorrhoid. We carried out this systematic review and meta-analysis of the randomized controlled trials (RCTs) to comprehensively evaluate the efficacy of harmonic scalpel (HS) versus bipolar diathermy (BD) methods in terms of decreasing intraoperative and postoperative morbidities among patients undergoing hemorrhoidectomy. Suitable citations were found utilizing digital medical sources, including the CENTRAL, Web of Science, PubMed, Scopus, and Google Scholar, from inception until December 2022. Only RCTs that matched the inclusion requirements were selected. We used the updated Cochrane risk of bias (ROB) tool (version 2) to assess the quality of the involved citations. The Review Manager (version 5.4 for Windows) was used to perform the pooled analysis. Data were pooled and reported as mean difference (MD) or risk ratio (RR) with a 95% confidence interval (CI) in random-effects models. Overall, there was no significant difference between HS and BD in terms of decreasing intraoperative morbidities like operative time, intraoperative blood loss, mean duration of hospital stay, and mean duration of first bowel movement (P>0.05). Similarly, the rate of postoperative complications like pain, bleeding, urinary retention, anal stenosis, flatus incontinence, and wound edema; was similar in both groups with no significant difference (P>0.05). In conclusion, our pooled analysis revealed there was no substantial difference between HS and BD in terms of intraoperative and postoperative endpoints. Additional RCTs with larger sample sizes are needed to consolidate the power and quality of the presented evidence.
ABSTRACT
Background: The high infection rate, severe symptoms, and evolving aspects of the COVID-19 pandemic provide challenges for a variety of medical systems around the world. Automatic information retrieval from unstructured text is greatly aided by Natural Language Processing (NLP), the primary approach taken in this field. This study addresses COVID-19 mortality data from the intensive care unit (ICU) in Kuwait during the first 18 months of the pandemic. A key goal is to extract and classify the primary and intermediate causes of death from electronic health records (EHRs) in a timely way. In addition, comorbid conditions or concurrent diseases were retrieved and analyzed in relation to a variety of causes of mortality. Method: An NLP system using the Python programming language is constructed to automate the process of extracting primary and secondary causes of death, as well as comorbidities. The system is capable of handling inaccurate and messy data, this includes inadequate formats, spelling mistakes and mispositioned information. A machine learning decision trees method is used to classify the causes of death. Results: For 54.8% of the 1691 ICU patients we studied, septic shock or sepsis-related multiorgan failure was the leading cause of mortality. About three-quarters of patients die from acute respiratory distress syndrome (ARDS), a common intermediate cause of death. An arrhythmia (AF) disorder was determined to be the strongest predictor of intermediate cause of death, whether caused by ARDS or other causes. Conclusion: We created an NLP system to automate the extraction of causes of death and comorbidities from EHRs. Our method processes messy and erroneous data and classifies the primary and intermediate causes of death of COVID-19 patients. We advocate arranging the EHR with well-defined sections and menu-driven options to reduce incorrect forms.
