Probability based criteria are proposed for the assessment of cost-effectiveness of a new treatment compared to a standard treatment when there are multiple effectiveness measures. Depending on the preferences of a policy maker, there are several options to define such criteria. Two such metrics are investigated in detail. One metric is defined as the conditional probability that a new treatment is more effective with respect to the multiple effectiveness measures for patients having lower costs under the new treatment. A second metric is defined as the conditional probability that a new treatment is less costly for patients having greater health benefits under the new treatment. The metrics offer considerable flexibility to a policy maker as thresholds for cost and effectiveness can be incorporated into the metrics. Parametric confidence limits are developed using a percentile bootstrap approach assuming multivariate normality for the joint distribution of the log(cost) and effectiveness measures. A non-parametric estimation procedure is also developed using the theory of U-statistics. Numerical results indicate that the proposed confidence limits accurately maintain coverage probabilities. The methodologies are illustrated using a study on the treatment of type two diabetes. Code implementing the proposed methods are provided in the supporting information.
Background: Delirium is common in hospitalized patients and correlated with increased morbidity and mortality. Despite this, delirium is underdiagnosed, and many institutions do not have sufficient resources to consistently apply effective screening and prevention.
Objective: To develop a machine learning algorithm to identify patients at highest risk of delirium in the hospital each day in an automated fashion based on data available in the electronic medical record, reducing the barrier to large-scale delirium screening.
Purpose: The assessment of knowledge gain from educational interventions is a common practice in dementia care research. This study aimed to refine and validate the Changing Talk Scale (CHATS), a tool for assessing knowledge of effective and person-centered communication in nursing home staff. CHATS was integrated into the Changing Talk: Online (CHATO) education program to measure the knowledge gains resulting from it.
Shenzhen Key Laboratory of Advanced Thin Films and Applications, College of Physics and optoelectronic engineering, Shenzhen University, Shenzhen, 518060, P. R. China.
Crystalline thermoelectric materials, especially SnSe crystals, have emerged as promising candidates for power generation and electronic cooling. In this study, significant enhancement in ZT is achieved through the combined effects of lattice distortions and band convergence in multiple electronic valence bands. Density functional theory (DFT) calculations demonstrate that cation vacancies together with Pb substitutional doping promote the band convergence and increase the density of states (DOS) near the Fermi surface of SnSe, leading to a notable increase in the Seebeck coefficient (S).
Purpose: To evaluate the diagnostic ability and methodological quality of ML models in detecting Pancreatic Ductal Adenocarcinoma (PDAC) in Contrast CT images.
Method: Included studies assessed adults diagnosed with PDAC, confirmed by histopathology. Metrics of tests were interpreted by ML algorithms.
School of Public Health, the key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang, 561113, China.
With the global prevalence of the hand-foot-and-mouth disease (HFMD) epidemic, the development of reliable point-of-care testing (POCT) is crucial for the timely identification and prevention of outbreaks. Isothermal nucleic acid amplification techniques (INAATs) have attracted much attention because of their high efficiency for rapid diagnosis. In this work, we systematically summarize the current status of INAATs for HFMD and discuss advantages and drawbacks of various INAATs for HFMD.
