PREVIEW; United States v. Havens: \u3cem\u3e“I Have an AR-15 & I Know How to Use It.”\u3c/em\u3e

The question presented here is whether defendant Joan Havens threatened, intimidated, or interfered with Forest Service employees when she warned them they would be shot if they trespassed on her property

MIMIC III and its contribution to critical care prediction models

Purpose - The present paper attempts to present the research that has been made on prediction models using deep learning methods with data retrieved from mimic III database and to identify challenges and possible areas for future research. Methodology - A literature research was conducted for articles related to MIMIC III and prediction models related to the database published from 2016 to 2021. Also, reviews and papers related to neural networks, machine learning, data mining and implementation and usage of electronic health records (EHR) in ICU were investigated to support findings from mimic III papers. Findings - Prediction algorithms can be very useful in ICU units. Although some algorithms, such as InSight are specialized in specific diseases, others such as XGBOOST and recurrent neural networks can be used in a broader area, presenting quite accurate results. Originality - Usually, reviews categorize research on MIMIC database per disease or per the desired outcome, such as the prediction of length of stay and the final outcome. The current study categorizes the research based on the tools, prediction models, and algorithms used. This way, it is possible to understand better how each method performs to various conditions and desired outcomes

Complete uterine inversion during caesarean section: A case report

Inversion of the uterus through the uterine lower segment incision during a caesarean section is an extremely rare obstetric incident. It consists, though, an emergency complication that is potentially life-threatening, especially in cases of prolonged inversion, because haemodynamic instability and shock may occur. Prompt diagnosis and immediate uterine reversion are the key actions in the management of this serious complication

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries