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.

Theories behind a nursing intern’s error in terms of clinical decision-making

Clinical decision-making and judgment is an essential skill that should be acquired by nursing interns.However,many studies reveal that a large proportion of nursing interns have difficulty in meeting the basic expectation of clinical decision-making.This article begins with a brief review of some theories related to clinical decision-making.Then it takes a case,for example,to show how the wrong decision is made.Furthermore,it makes a general analysis on the case drawing on the concept of bounded rationality,dual-process model,and skills acquisition theory.This article ends with some reflections from the case,to improve the consciousness and skills in decision-making for nursing interns.

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.

Challenges related to clinical decision-making in hepatocellular carcinoma recurrence post-liver transplantation: Is there a hope?

Hepatocellular carcinoma(HCC)is a common liver malignancy and represents a serious cause of cancer-related mortality and morbidity.One of the favourable curative surgical therapeutic options for HCC is liver transplantation(LT)in selected patients fulfilling the known standard Milan/University of California San Francisco criteria which have shown better outcomes and longer-term survival.Despite careful adherence to the strict HCC selection criteria for LT in different transplant centres,the recurrence rate still occurs which could negatively affect HCC patients’survival.Hence HCC recurrence post-LT could predict patients’survival and prognosis,depending on the exact timing of recurrence after LT(early or late),and whether intra/extrahepatic HCC recurrence.Several factors may aid in such a complication,particularly tumour-related criteria including larger sizes,higher grades or poor tumour differentiation,microvascular invasion,and elevated serum alpha-fetoprotein.Therefore,managing such cases is challenging,different therapeutic options have been proposed,including curative surgical and ablative treatments that have shown better outcomes,compared to the palliative locoregional and systemic therapies,which may be helpful in those with unresectable tumour burden.To handle all these issues in our review.

Clinical decision-making by the emergency department resident physicians for critically ill patients

The application of main methodologies for clinical decision-making by residents in emergency medical practice was assessed,and issues in this area were investigated.The treatments provided to 2611 critical patients by the Emergency Department of Peking Union Medical College Hospital were analyzed by independent investigators who evaluated the main clinical decision-making processes applied by the hospital residents.The application of decision-making strategies by PG1 and PG3 groups,which means the residents in first year and the third year,were compared.The patients were treated according to pattern recognition(43.0%),hypotheticodeductive reasoning(23.4%),event-driven models(19.3%),and rule-using algorithms(5.9%).A significant difference was found between PG1 and PG3 groups(χ^(2)=498.01,P<0.001).Pattern recognition and hypotheticdeductive methods were the most common techniques applied by emergency physicians in evaluating critically ill patients.The decision-making processes applied by junior and senior residents were significantly different,although neither group adequately applied rule-using algorithms.Inclusion of clinical decision-making in medical curricula is needed to improve decision-making in critical care.

Three Basic Modes for Patients' Clinical Decision-Making in China

In China,there are three basic clinical decision-making modes for patients,namely patients autonomous decision-making mode,family decision-making mode and patient and family codetermination.They were produced under the unique background of Chinese medicine,Confucian philosophy and law in China,l this paper,the concepts,advantages and disadvantages of these three decision-making modes were analyzed In addition,some suggestions were put forward for the improvement.The first is that we suggest to establis standards for choosing decision-making modes;the second is to further learn and publicize relevant laws;thirdly the legal system needs to be further refined;and the last one is to carry out ethical ward round.

Evolutionary Decision-Making and Planning for Autonomous Driving Based on Safe and Rational Exploration and Exploitation

Decision-making and motion planning are extremely important in autonomous driving to ensure safe driving in a real-world environment.This study proposes an online evolutionary decision-making and motion planning framework for autonomous driving based on a hybrid data-and model-driven method.First,a data-driven decision-making module based on deep reinforcement learning(DRL)is developed to pursue a rational driving performance as much as possible.Then,model predictive control(MPC)is employed to execute both longitudinal and lateral motion planning tasks.Multiple constraints are defined according to the vehicle’s physical limit to meet the driving task requirements.Finally,two principles of safety and rationality for the self-evolution of autonomous driving are proposed.A motion envelope is established and embedded into a rational exploration and exploitation scheme,which filters out unreasonable experiences by masking unsafe actions so as to collect high-quality training data for the DRL agent.Experiments with a high-fidelity vehicle model and MATLAB/Simulink co-simulation environment are conducted,and the results show that the proposed online-evolution framework is able to generate safer,more rational,and more efficient driving action in a real-world environment.

Clinical study of different prediction models in predicting diabetic nephropathy in patients with type 2 diabetes mellitus

BACKGROUND Among older adults,type 2 diabetes mellitus(T2DM)is widely recognized as one of the most prevalent diseases.Diabetic nephropathy(DN)is a frequent com-plication of DM,mainly characterized by renal microvascular damage.Early detection,aggressive prevention,and cure of DN are key to improving prognosis.Establishing a diagnostic and predictive model for DN is crucial in auxiliary diagnosis.AIM To investigate the factors that impact T2DM complicated with DN and utilize this information to develop a predictive model.METHODS The clinical data of 210 patients diagnosed with T2DM and admitted to the First People’s Hospital of Wenling between August 2019 and August 2022 were retrospectively analyzed.According to whether the patients had DN,they were divided into the DN group(complicated with DN)and the non-DN group(without DN).Multivariate logistic regression analysis was used to explore factors affecting DN in patients with T2DM.The data were randomly split into a training set(n=147)and a test set(n=63)in a 7:3 ratio using a random function.The training set was used to construct the nomogram,decision tree,and random forest models,and the test set was used to evaluate the prediction performance of the model by comparing the sensitivity,specificity,accuracy,recall,precision,and area under the receiver operating characteristic curve.RESULTS Among the 210 patients with T2DM,74(35.34%)had DN.The validation dataset showed that the accuracies of the nomogram,decision tree,and random forest models in predicting DN in patients with T2DM were 0.746,0.714,and 0.730,respectively.The sensitivities were 0.710,0.710,and 0.806,respectively;the specificities were 0.844,0.875,and 0.844,respectively;the area under the receiver operating characteristic curve(AUC)of the patients were 0.811,0.735,and 0.850,respectively.The Delong test results revealed that the AUC values of the decision tree model were lower than those of the random forest and nomogram models(P<0.05),whereas the difference in AUC values of the random forest and column-line graph models was not statistically significant(P>0.05).CONCLUSION Among the three prediction models,random forest performs best and can help identify patients with T2DM at high risk of DN.

Epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China:a hospital-based retrospective study

Traumatic spinal cord injury is potentially catastrophic and can lead to permanent disability or even death.China has the largest population of patients with traumatic spinal cord injury.Previous studies of traumatic spinal cord injury in China have mostly been regional in scope;national-level studies have been rare.To the best of our knowledge,no national-level study of treatment status and economic burden has been performed.This retrospective study aimed to examine the epidemiological and clinical features,treatment status,and economic burden of traumatic spinal cord injury in China at the national level.We included 13,465 traumatic spinal cord injury patients who were injured between January 2013 and December 2018 and treated in 30 hospitals in 11 provinces/municipalities representing all geographical divisions of China.Patient epidemiological and clinical features,treatment status,and total and daily costs were recorded.Trends in the percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department and cost of care were assessed by annual percentage change using the Joinpoint Regression Program.The percentage of traumatic spinal cord injuries among all hospitalized patients and among patients hospitalized in the orthopedic department did not significantly change overall(annual percentage change,-0.5%and 2.1%,respectively).A total of 10,053(74.7%)patients underwent surgery.Only 2.8%of patients who underwent surgery did so within 24 hours of injury.A total of 2005(14.9%)patients were treated with high-dose(≥500 mg)methylprednisolone sodium succinate/methylprednisolone(MPSS/MP);615(4.6%)received it within 8 hours.The total cost for acute traumatic spinal cord injury decreased over the study period(-4.7%),while daily cost did not significantly change(1.0%increase).Our findings indicate that public health initiatives should aim at improving hospitals’ability to complete early surgery within 24 hours,which is associated with improved sensorimotor recovery,increasing the awareness rate of clinical guidelines related to high-dose MPSS/MP to reduce the use of the treatment with insufficient evidence.

Toward Trustworthy Decision-Making for Autonomous Vehicles:A Robust Reinforcement Learning Approach with Safety Guarantees

While autonomous vehicles are vital components of intelligent transportation systems,ensuring the trustworthiness of decision-making remains a substantial challenge in realizing autonomous driving.Therefore,we present a novel robust reinforcement learning approach with safety guarantees to attain trustworthy decision-making for autonomous vehicles.The proposed technique ensures decision trustworthiness in terms of policy robustness and collision safety.Specifically,an adversary model is learned online to simulate the worst-case uncertainty by approximating the optimal adversarial perturbations on the observed states and environmental dynamics.In addition,an adversarial robust actor-critic algorithm is developed to enable the agent to learn robust policies against perturbations in observations and dynamics.Moreover,we devise a safety mask to guarantee the collision safety of the autonomous driving agent during both the training and testing processes using an interpretable knowledge model known as the Responsibility-Sensitive Safety Model.Finally,the proposed approach is evaluated through both simulations and experiments.These results indicate that the autonomous driving agent can make trustworthy decisions and drastically reduce the number of collisions through robust safety policies.

Orientation and Decision-Making for Soccer Based on Sports Analytics and AI:A Systematic Review

Due to ever-growing soccer data collection approaches and progressing artificial intelligence(AI) methods, soccer analysis, evaluation, and decision-making have received increasing interest from not only the professional sports analytics realm but also the academic AI research community. AI brings gamechanging approaches for soccer analytics where soccer has been a typical benchmark for AI research. The combination has been an emerging topic. In this paper, soccer match analytics are taken as a complete observation-orientation-decision-action(OODA) loop.In addition, as in AI frameworks such as that for reinforcement learning, interacting with a virtual environment enables an evolving model. Therefore, both soccer analytics in the real world and virtual domains are discussed. With the intersection of the OODA loop and the real-virtual domains, available soccer data, including event and tracking data, and diverse orientation and decisionmaking models for both real-world and virtual soccer matches are comprehensively reviewed. Finally, some promising directions in this interdisciplinary area are pointed out. It is claimed that paradigms for both professional sports analytics and AI research could be combined. Moreover, it is quite promising to bridge the gap between the real and virtual domains for soccer match analysis and decision-making.

Ethical Decision-Making Framework Based on Incremental ILP Considering Conflicts

Humans are experiencing the inclusion of artificial agents in their lives,such as unmanned vehicles,service robots,voice assistants,and intelligent medical care.If the artificial agents cannot align with social values or make ethical decisions,they may not meet the expectations of humans.Traditionally,an ethical decision-making framework is constructed by rule-based or statistical approaches.In this paper,we propose an ethical decision-making framework based on incremental ILP(Inductive Logic Programming),which can overcome the brittleness of rule-based approaches and little interpretability of statistical approaches.As the current incremental ILP makes it difficult to solve conflicts,we propose a novel ethical decision-making framework considering conflicts in this paper,which adopts our proposed incremental ILP system.The framework consists of two processes:the learning process and the deduction process.The first process records bottom clauses with their score functions and learns rules guided by the entailment and the score function.The second process obtains an ethical decision based on the rules.In an ethical scenario about chatbots for teenagers’mental health,we verify that our framework can learn ethical rules and make ethical decisions.Besides,we extract incremental ILP from the framework and compare it with the state-of-the-art ILP systems based on ASP(Answer Set Programming)focusing on conflict resolution.The results of comparisons show that our proposed system can generate better-quality rules than most other systems.

Clinical and Therapeutic Aspects of Migraine in Brazzaville

Introduction: Migraine is the most common primary headache, and can cause significant disability. There are two types, migraine without aura and migraine with aura. The diagnosis of migraine is essentially clinical. Worldwide prevalence was estimated at 11.6% in 2009. In Africa, it is estimated at 10.4%. Objective: To describe the clinical and therapeutic aspects of migraine in Brazzaville. Patients and Methods: This was a door-to-door cross-sectional study conducted from 1st May to 1 st July 2018 in the city of Brazzaville. Subjects over 18 with clearly expressed consent were included. The questionnaire covered demographic characteristics, diagnostic criteria for migraine according to the IHS, treatments taken. The degree of disability was determined using the Migraine Disability Assessment Scale (MIDAS). Statistical analysis was performed using SPSS 22.0 for MAC. Results: Of the 1017 subjects interviewed in this study, 115 (39.9%) had migraine, including 73 women (63.47%) and 42 men (36.52%). In the group of migraine sufferers, the number of cases of definite migraine was 61 (53.04%) and that of probable migraine 54 (46.95%). For 81 migraine sufferers (70.43%), stress was the triggering factor. The frequency of attacks was weekly and monthly for 30 (26.1%) and 19 (16.5%) sufferers respectively. The location of the migraine was unilateral in 38% of cases and tilted in 24.3%. The intensity of the attack was described as moderate and severe in 41.7% and 57.4% of subjects respectively. Phonophobia/photophobia accompanied the migraine in 65.2% of cases. One hundred and eight subjects were treated. Of these, 106 (98.1%) were on medication. Eleven (10.37%) had received a medical prescription, and ninety-seven (89.8%) were self-medicating. Five and three subjects were under the care of a general practitioner and a neurologist respectively. Conclusion: Migraine is a frequent pathology in Brazzaville. Its preponderance among young people and women calls for the implementation of effective prevention strategies for these already vulnerable social groups. The form without aura was the most common type. Visual aura was the most common type. Headache-related symptoms were dominated by phonophotophobia, followed by nausea and vomiting. Almost all migraine sufferers were self-medicating, and very few were under the care of a doctor. First-line analgesics and NSAIDs were the mainstay of treatment.

Artificial intelligence analysis of videos to augment clinical assessment:an overview

Observation is a fundamental part of the practice of clinical medicine.Observation of movement is particularly important for the neurologist.Conditions such as Parkinson’s disease,multiple sclerosis,stroke,epilepsy,and many others affect a person’s movement in characteristic ways.In some conditions,changes in the patient’s voice can be included in this-changes in sound caused by changes in the movements of speech.The clinician’s detection of a characteristic abnormality,and their judgment of its severity,plays a central role in both diagnosis and the assessment of prognosis or response to treatment.However,that practice depends upon a limited resource of experienced experts.In addition,these experts are limited by human visual judgment,which cannot reliably or precisely detect and measure small or subtle changes in movement(Williams et al.,2023).

Molecular Epidemiology and Clinical Characteristics of Hand, Foot and Mouth Disease in North Sichuan Region, China, 2018-2023: A Descriptive Study

Background: Hand, foot and mouth disease (HFMD) remains an important public health problem in China. Many studies on the epidemiological characteristics of HFMD have been reported, but studies in North Sichuan region have been neglected. Methods: HFMD-related enterovirus infected cases were clinically confirmed and underwent real-time RT-PCR (rRT-PCR) from May 2018 to October 2023 in Guangyuan Central Hospital. Results: During 2018-2023, other EV (437 cases, 81.08%) was the most predominant serotype followed by CV-A16 (94 cases, 17.44%), EV-A71 (8 cases, 1.48%) was the least predominant serotype. Peak infections occurred in July and October. There were no significant differences in gender, age and serotypes. HFMD was concentrated in children under 47 months of age, with the highest incidence in children aged 12 - 23 months and the highest proportion of other EV infections in the whole age group. COVID-19 did not cause significant changes in gender, age and serotype. Overall, there was a significant increase in the proportion of children aged 12 - 23 months infected with CV-A16, and an increase in the proportion of children aged over 36 months infected with other EVs. Conclusions: The incidence of HFMD caused by EV-A71 has decreased significantly, and other EVs have become the main pathogens of HFMD in North Sichuan region in recent years. In the prevention and control of CV-A16, more attention should be paid to children aged 12 - 23 months and the dominant serotype should be closely monitored. Our study highlights the importance of developing of new diagnostic reagents and vaccines for the prevention and control of enterovirus infection. This study for the first time provides insights into district interventions to local conditions.

Stroke Risk Assessment Decision-Making Using a Machine Learning Model:Logistic-AdaBoost

Stroke is a chronic cerebrovascular disease that carries a high risk.Stroke risk assessment is of great significance in preventing,reversing and reducing the spread and the health hazards caused by stroke.Aiming to objectively predict and identify strokes,this paper proposes a new stroke risk assessment decision-making model named Logistic-AdaBoost(Logistic-AB)based on machine learning.First,the categorical boosting(CatBoost)method is used to perform feature selection for all features of stroke,and 8 main features are selected to form a new index evaluation system to predict the risk of stroke.Second,the borderline synthetic minority oversampling technique(SMOTE)algorithm is applied to transform the unbalanced stroke dataset into a balanced dataset.Finally,the stroke risk assessment decision-makingmodel Logistic-AB is constructed,and the overall prediction performance of this new model is evaluated by comparing it with ten other similar models.The comparison results show that the new model proposed in this paper performs better than the two single algorithms(logistic regression and AdaBoost)on the four indicators of recall,precision,F1 score,and accuracy,and the overall performance of the proposed model is better than that of common machine learning algorithms.The Logistic-AB model presented in this paper can more accurately predict patients’stroke risk.

Mission of the National Cancer Center Hospital in Japan to promote clinical trials for precision medicine

Precision medicine is a growing field worldwide.Despite its potential benefit to many patients,several major obstacles must be overcome before precision medicine can be more widely used in clinical practice.The main obstacles are associated with the quality of samples used for genomic analysis。

Stress Echocardiography for Chronic Coronary Syndrome:Clinical Practice Guidelines(2023)

In the context of the People's Republic of China,coronary artery disease(CAD)presents a sig-nificant clinical challenge,with over 11.3 mil-lion patients diagnosed.Traditionally,the diagnos-is of CAD has predominantly relied on invasive coronary angiography.[1]However,recent advances in clinical research have revealed a notable trend:a substantial 82% of patients subjected to such invas-ive diagnostics do not necessitate interventional therapy.

Methodological considerations of priming repetitive transcranial magnetic stimulation protocols in clinical populations

INTRODUCTION Repetitive transcranial magnetic stimulation(rTMS)is a neuroplasticity-enhancing technique that modifies brain responsiveness to various therapeutic modalities in clinical psychiatric and neurological applications. Furthermore,its effect can be attributed to long-term potentiation(LTP)or longterm depression(LTD)-like neuroplasticity.However,responsiveness to rTMS is largely variable in healthy and pathological brains and is mediated by complex biological mechanisms.Metaplasticity refers to a higher-order plasticity mechanism in which the direction and magnitude of synaptic plasticity are modified by prior neuronal activity and is believed to be a significant factor leading to the response variability of rTMs.

Chlorfenapyr poisoning:mechanisms,clinical presentations,and treatment strategies

BACKGROUND:Chlorfenapyr is used to kill insects that are resistant to organophosphorus insecticides.Chlorfenapyr poisoning has a high mortality rate and is difficult to treat.This article aims to review the mechanisms,clinical presentations,and treatment strategies for chlorfenapyr poisoning.DATA RESOURCES:We conducted a review of the literature using PubMed,Web of Science,and SpringerLink from their beginnings to the end of October 2023.The inclusion criteria were systematic reviews,clinical guidelines,retrospective studies,and case reports on chlorfenapyr poisoning that focused on its mechanisms,clinical presentations,and treatment strategies.The references in the included studies were also examined to identify additional sources.RESULTS:We included 57 studies in this review.Chlorfenapyr can be degraded into tralopyril,which is more toxic and reduces energy production by inhibiting the conversion of adenosine diphosphate to adenosine triphosphate.High fever and altered mental status are characteristic clinical presentations of chlorfenapyr poisoning.Once it occurs,respiratory failure occurs immediately,ultimately leading to cardiac arrest and death.Chlorfenapyr poisoning is diflcult to treat,and there is no specific antidote.CONCLUSION:Chlorfenapyr is a new pyrrole pesticide.Although it has been identified as a moderately toxic pesticide by the World Health Organization(WHO),the mortality rate of poisoned patients is extremely high.There is no specific antidote for chlorfenapyr poisoning.Therefore,based on the literature review,future efforts to explore rapid and effective detoxification methods,reconstitute intracellular oxidative phosphorylation couplings,identify early biomarkers of chlorfenapyr poisoning,and block the conversion of chlorfenapyr to tralopyril may be helpful for emergency physicians in the diagnosis and treatment of this disease.