CBL联合PBL带教模式在本科实习带教中的应用

目的探究案例式教学(case-based learning,CBL)联合探讨问题式学习(problem-based learning,PBL)带教模式在临床本科生实习带教中的应用效果,为实习生的临床带教提供指导。方法选取2019年1月—2019年6月来本科实习的60名临床本科实习生进行研究。按照简单随机分组法分为观察组和对照组,每组各30名。对照组进行传统的教学方法,而观察组进行CBL联合PBL的教学方法。实习期满后,对两组实习生进行综合测评,并通过问卷调查的方式了解对学生对带教方法的教学效果评价以及教学满意度。结果观察组实习生的基础知识测试成绩和技能操作成绩均明显高于对照组,观察组学生的课堂活跃度、学习兴趣以及知识理解能力评分均明显高于对照组,实习生对带教满意率明显高于对照组,差异有统计学意义(P<0.05)。结论CBL联合PBL的教学模式有助于学生对知识的理解,加强动手操作的能力,能显著提高实习生的理论及操作成绩,提升临床的教学质量及教学满意度。     

三轨教学模式在肿瘤内科临床教学中的应用价值

目的分析肿瘤内科临床教学中三轨教学模式的应用价值。方法采用分层抽样法抽选2018年1月-2018年12月在本院实习的学生共68名纳入本次实验,并用均等双盲划分法将其中34例纳入对照组(采取传统教学法),剩余34例纳入观察组(采取三轨教学模式),观察两组教学效果。结果观察组理论及技能操作考核成绩比对照组更高,同时在提升学习积极性、掌握理论知识、培养临床能力、培养思维能力、培养自学能力、提升文献查阅能力、提升团队协作力、提升分析解决问题能力方面评分均高于对照组(P<0.05)。结论肿瘤内科的临床教学中采取三轨教学模式效果显著,能调动学生的主观能动性,提升其理论知识及技能操作水平,培养其临床实践能力及临床思维能力,值得采用。     

机器学习算法在麻醉领域的应用

人工智能在医疗领域的应用逐步推动医疗变革,机器学习算法融入麻醉领域对于进一步提升麻醉发展意义重大。为了解机器学习算法在麻醉领域的应用现况及推动相关研究进展,本文总结了机器学习在围手术期麻醉管理和预测术后并发症方面的应用、基本概念和研究现状,指出了目前机器学习算法在医疗领域的不足之处。     

Patient Management Assisted by a Neural Network Reduces Mortality in an Intermediate Care Unit

IntroductionMortality risk prediction for Intermediate Respiratory Care Unit's (IRCU) patients can facilitate optimal treatment in high-risk patients. While Intensive Care Units (ICUs) have a long term experience in using algorithms for this purpose, due to the special features of the IRCUs, the same strategics are not applicable. The aim of this study is to develop an IRCU specific mortality predictor tool using machine learning methods.MethodsVital signs of patients were recorded from 1966 patients admitted from 2007 to 2017 in the Jiménez Díaz Foundation University Hospital's IRCU. A neural network was used to select the variables that better predict mortality status. Multivariate logistic regression provided us cut-off points that best discriminated the mortality status for each of the parameters. A new guideline for risk assessment was applied and mortality was recorded during one year.ResultsOur algorithm shows that thrombocytopenia, metabolic acidosis, anemia, tachypnea, age, sodium levels, hypoxemia, leukocytopenia and hyperkalemia are the most relevant parameters associated with mortality. First year with this decision scene showed a decrease in failure rate of a 50%.ConclusionsWe have generated a neural network model capable of identifying and classifying mortality predictors in the IRCU of a general hospital. Combined with multivariate regression analysis, it has provided us with an useful tool for the real-time monitoring of patients to detect specific mortality risks. The overall algorithm can be scaled to any type of unit offering personalized results and will increase accuracy over time when more patients are included to the cohorts.     

K-Means Clustering to Elucidate Vulnerable Subpopulations Among Medicare Patients Undergoing Total Joint Arthroplasty

BackgroundThe role of preoperative laboratory values for risk stratification following joint arthroplasty is currently ambiguous. In order to improve upon existing risk stratification within joint arthroplasty, this study sought to define novel phenotypes of total hip or total knee arthroplasty patients based entirely on preoperative laboratory measures. These phenotypes (“clusters”) were compared to elucidate statistically and clinically significant differences in outcomes.MethodsA total of 134,252 patients were gathered from the National Surgical Quality Improvement Program database between 2005 and 2015. “K-means” with 3 clusters was applied using 9 preoperative laboratory values: sodium, blood urea nitrogen (BUN), creatinine, albumin, bilirubin, white blood cell count, hematocrit, platelet count, and international normalized ratio of prothrombin values (INR). Outcome measures included 30-day readmissions, severe adverse events, and discharge to nonhome.ResultsCluster 2 was characterized by elevated preoperative BUN, creatinine, and INR and demonstrated almost twice the rate of adverse events (3.52% vs 2.20% and 2.22%), 30-day readmissions (6.39% vs 3.31% and 3.71%), and discharge to nonhome (47.97% vs 30.50% and 35.85%). Cluster 3 was characterized by a slightly higher risk of discharge to nonhome than cluster 1 and was overwhelmingly female (79.5% female, 35.8% discharge to nonhome). Cluster 1 represents the lowest-risk subgroup, experiencing the lowest rates of readmissions, adverse events, and discharge to nonhome.ConclusionPreoperative laboratory values, namely BUN, creatinine, and INR, are useful in identifying patients at risk of adverse outcomes. This analysis supports the existing surgical literature pushing for preoperative hydration as a targeted intervention to expedite recovery.