Effectiveness of simulation-based learning regarding management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students:A quasi-experimental study

Objective:To assess the effectiveness of simulation-based learning regarding the management of post-COVID complications in terms of knowledge,clinical decision-making ability,and self-efficacy among nursing students.Methods:This was a quasi-experimental study conducted among 1152nd-year nursing students.The participants were selected by a simple random sampling technique.The participants were divided into an experimental(n=56)and a comparison group(n=59)by a random table method.Data were analyzed using descriptive and inferential statistics with SPSS version 20.Results:There were significant differences in mean post-test knowledge scores(P=0.03)and mean post-test self-efficacy scores(P=0.001)between the experimental and the comparison groups while the difference in mean post-test clinical decision-making ability scores between the two groups was non-significant(P=0.07).A positive correlation was found between knowledge and clinical decision-making ability in pre-test(P=0.03)and in post-test(P<0.001)and a non-significant correlation was found between pre-test knowledge and self-efficacy score(P=0.52)among the experimental group.Conclusions:Simulation-based learning regarding the management of post-COVID complications is effective among nursing students.Simulation labs should be established in health care settings where simulation training can be provided for updating the knowledge,clinical decision-making ability,and self-efficacy of nursing personnel during program installment and continuous nursing education.

Current advancements in application of artificial intelligence in clinical decision-making by gastroenterologists in gastrointestinal bleeding

Artificial Intelligence(AI)is a type of intelligence that comes from machines or computer systems that mimics human cognitive function.Recently,AI has been utilized in medicine and helped clinicians make clinical decisions.In gastroenterology,AI has assisted colon polyp detection,optical biopsy,and diagnosis of Helicobacter pylori infection.AI also has a broad role in the clinical prediction and management of gastrointestinal bleeding.Machine learning can determine the clinical risk of upper and lower gastrointestinal bleeding.AI can assist the management of gastrointestinal bleeding by identifying high-risk patients who might need urgent endoscopic treatment or blood transfusion,determining bleeding stigmata during endoscopy,and predicting recurrence of gastrointestinal bleeding.The present review will discuss the role of AI in the clinical prediction and management of gastrointestinal bleeding,primarily on how it could assist gastroenterologists in their clinical decision-making compared to conventional methods.This review will also discuss challenges in implementing AI in routine practice.

Prognostic value of decision criteria for emergency liver transplantation in patients with wild mushroom induced acute liver injury

Background： The reported mortality rate of mushroom-induced acute liver failure with conventionaltreatment is 1.4%–16.9%. Emergency liver transplantation may be indicated and can be the only curativetreatment option. This study aimed to assess the prognostic value of criteria for emergency livertransplantation in predicting 28-day mortality in patients with mushroom-induced acute liver injury.Methods： A retrospective cohort study was performed between January 2005 and December 2015. Alladult patients aged≥18 years admitted with mushroom intoxication at our emergency department wereevaluated. All patients with acute liver injury, defined as elevation of serum liver enzymes （〉5 timesthe upper limit of normal, ULN） or moderate coagulopathy （INR 〉 2.0） were included. The ability of the King’s College, Ganzert’s, and Escudié’s criteria to predict 28-day mortality was evaluated.

Making Evidence-based Decisions in the Clinical Practice of Integrative Medicine

In 1948, the first clinical paper adopting the protocol of randomized and controlled design was published in British Medical Journal by Bradford Hill,a noted British biostatistician, who introduced rigorous theory of mathematical statistics into clinical design the first time and successfully evaluated the therapeutic effect of streptomycin on tuberculosis.

COVID-19 pandemic:Building organisational flexibility to scale transplant programs

The prevailing coronavirus disease 2019 pandemic has challenged our lives in an unprecedented manner.The pandemic has had a significant impact on transplantation worldwide.The logistics of travel restrictions,stretching of available resources,unclear risk of infection in immunosuppressed transplant recipients,and evolving guidelines on testing and transplantation are some of the factors that have unfavourably influenced transplant activity.We must begin to build organisational flexibility in order to restart transplantation so that we can be mindful stewards of organ donation and sincere advocates for our patients.Building a culture of honesty and transparency(with patients,families,colleagues,societies,and authorities),keeping the channels of communication open,working in collaboration with others(at local,regional,national,and international levels),and not restarting without rethinking and appraising all elements of our practice,are the main underlying principles to increase the flexibility.

医患共同决策在妇产科的应用与探讨

临床共同决策是医患双方共同参与,医务人员充分告知决策利弊,权衡风险,基于患者个人意愿、偏好及价值观念,发挥患者在医疗决策中的主观能动性,主动反映决策中自身的观点,最终达到医患双方决策的一致性。本文就临床共同决策在妇产科中的应用进行论述,明确临床共同决策在妇产科疾病中的应用价值。