Simulation-based medical education is becoming increasingly common. In this paper, the status and goal of SBME development is analyzed after a brief introduction of SBME. Secondly, the essentiality and possibility of bringing SBME to a situated paradigm are clarified, because there are rich implications for situated cognition as the theory foundation of SBME. As a main discussion point, eight practical situated designing principles for SBME in theoretical and practical contexts are then expounded. Finally, a specific attitude toward the relationship between theory and practice for the SBME teachers is also elucidated.
Prosthetics and orthotics is an applied discipline which require students to have a good ability of clinical application. However, the present doctor-patient tension poses great difficulties in students’ clinical practice and study, and thus proposes higher requirements in the talent cultivation. Standardized patients (SP), as a newly established teaching mode, has been applied well in such areas as internal medicine, diagnostics and clinical care for about 20 years in this country. However, in prosthetics and orthotics, we hardly have any SP teaching experience in China. According to the experience of using SP in clinical medicine, this article discusses the necessity of SP in the teaching of prosthetics and orthotics, explains the advantages and disadvantages of SP teaching, and analyzes the feasibility of SP application in the teaching of prosthetics and orthotics under the present situation. Finally, this article concludes that using SP can improve students’ motivation, interest in learning and communication skills with patients. Therefore, it will become a research direction and development trend of prosthetics and orthotics.
Objectives To discuss the methodology of evaluation of traditional Chinese medicine (TCM) placebo simulation effects and the problems of blind implementation and so as to improve the quality of double-blind clinical trials of TCM. Methods Focusing on case of placebo preparation of TCM investigational new drug, simulation effects of the placebo were evaluated in terms of shape, color, taste and smell. The possibility of placebo be a drug and the similarity between placebo and drug were tested. Results There was no significant difference between placebo and investigational new drug to be judged as a drug (P>0.05). As for the similarity between placebo and drug, there was no significantly difference of the shape (P>0.05), for which the similarity was 100%. The color, taste and smell were significant different between placebo and drug (P<0.05), for which the similarity were 50%, 10% and 15% respectively. Conclusions It is very difficult to simulate TCM based on its certain color, taste or smell. Therefore, the subjects and the investigators’ compliance should be kept to avoid breaking the blind intentionally in the process of the trial and the influence of unblinding should be estimated at the end of the trial.
ObjectivesTo explore the value of neural networks (NN) in estimating propensity score, and to compare the performance of propensity score methods based on both logistic regression (LR) and NN.MethodsData sets including ten binary or continuous covariates, binary treatment variable and continuous outcome variable were simulated by SAS 9.2 software, and 5 scenarios differing by non-linear and/or non-additive associations between treatment assignment and covariates were set up. The sample sizes 500, 1000, 2000, 5000 and 10000 were considered. Propensity scores were estimated using either LR or NN model using only partial covariates associated with the outcome (methods LR1, NN1), or all covariates associated with either outcome or treatment (methods LR2, NN2). The average treatment effect (ATE) estimates, standard error (SE), bias, and mean square error (MSE) of ATE among the different models were compared.ResultsThe 95% confidence intervals of the average treatment effect were narrower in NN than that in LR models. SE, bias and MSE increased with the increasing complexity of non-linear and/or non-additive associations between the treatment and covariates, and smaller SE, bias, and MSE were observed in LR1 than LR2, and in NN1 than NN2. NN generally produced less bias than LR under most scenarios when variables associated with the outcome were introduced. SE and MSE decreased with the increasing sample size for both LR and NN models.ConclusionsNN for estimating propensity scores may be less biased and produce more precise estimates for ATE than LR in a meaningful manner when the complex association between treatment and covariates exists.
ObjectivesTo systematically review the teaching efficacy of cardiac ultrasound simulation.MethodsCNKI, VIP, PubMed, EMbase and The Cochrane Library databases were electronically searched to collect randomized controlled trials (RCTs) on cardiac ultrasound simulation from inception to March 7th, 2020. Two reviewers independently screened literature, extracted data and assessed risk of bias of included studies, then, meta-analysis was performed by using RevMan 5.3 software.ResultsA total of 7 RCTs involving 300 trainees were included. The results of meta-analysis showed that: compared with traditional teaching method, trainees who received cardiac ultrasound simulation obtained higher cardiac ultrasonic structure image recognition score (SMD=1.38, 95%CI 0.81 to 1.94, P<0.000 01), higher ultrasonic image quality score (SMD=2.08, 95%CI 1.71 to 2.44, P<0.000 01), and shorter time required to obtain the correct ultrasound image (SMD=−1.19, 95%CI −1.55 to −0.83, P<0.000 01).ConclusionsThe current evidence shows that trainees who received cardiac ultrasound simulation have superior teaching effect immediately after the training compared with those who received traditional teaching method. However, further high-quality researches are needed to confirm whether there is a difference between the two training methods in long-term teaching effect.
ObjectiveA simulation study was used to generate the multivariate normal distribution data with a residual effect based on series of N-of-1 trials. The statistical performance of paired t-test, mixed effect model and Bayesian mixed effect model were compared.MethodsThree-cycles N-of-1 trials were set, and the participants were randomly assigned to 2 different treatments in each cycle. The simulation study included the following procedures: producing six-dimensional normal distribution data, randomly allocating intervention methods and patients, adding residual effects, constructing and evaluating 3 models, and setting the parameters. The sample sizes were set as 3, 5, 8 and 10, and the correlation coefficients among different times were set as 0.0, 0.5 and 0.8. Different proportions of residual effects for the 2 groups were set. Type I error, power, mean error (ME), and mean square error (MSE) were used to compare the 3 models.ResultsWhen there was no residual effect in the 2 groups, type I errors of 3 models were approximately 0.05, and their MEs were approximately 0. Paired t-test had the highest power and the lowest MSE. When the residual effect existed in the 2 groups, the type I error of paired t-test increased, and its estimated value deviated from the true value (ME≠0). Type I errors of the mixed effect model and Bayesian mixed-effect model were approximately 0.05, and they had the same power. The estimated values of the two models were close to the true value (ME was approximately 0).ConclusionsWhen there is no residual effect (0% vs. 0%), paired t-test is suitable for data analysis of N-of-1 trials. When there is a residual effect, the mixed effect model and Bayesian mixed-effect model are suitable for data analysis of N-of-1 trials.
ObjectiveWhen using multi-center data to construct clinical prediction models, the independence assumption of data will be violated, and there is an obvious clustering effect among research objects. In order to fully consider the clustering effect, this study intends to compare the model performance of the random intercept logistic regression model (RI) and the fixed effects model (FEM) considering the clustering effect with the standard logistic regression model (SLR) and the random forest algorithm (RF) without considering the clustering effect under different scenarios. MethodsIn the process of forecasting model establishment, the prediction performance of different models at the center level was simulated when there were different degrees of clustering effects, including the difference of discrimination and calibration in different scenarios, and the change trend of this difference at different event rates was compared. ResultsAt the center level, different models, except RF, showed little difference in the discrimination of different scenarios under the clustering effect, and the mean of their C-index changed very little. When using multi-center highly clustered data for forecasting, the marginal forecasts (M.RI, SLR and RF) had calibrated intercepts slightly less than 0 compared with the conditional forecasts, which overestimated the average probability of prediction. RF performed well in intercept calibration under the condition of multi-center and large samples, which also reflected the advantage of machine learning algorithm for processing large sample data. When there were few multiple patients in the center, the FEM made conditional predictions, the calibrated intercept was greater than 0, and the predicted mean probability was underestimated. In addition, when the multi-center large sample data were used to develop the prediction model, the slopes of the three conditional forecasts (FEM, A.RI, C.RI) were well calibrated, while the calibrated slopes of the marginal forecasts (M.RI and SLR) were greater than 1, which led to the problem of underfitting, and the underfitting problem became more prominent with the increase in the central aggregation effect. In particular, when there were few centers and few patients, overfitting of the data could mask the difference in calibration performance between marginal and conditional forecasts. Finally, the lower the event rate the central clustering effect at the central level had a more pronounced impact on the forecasting performance of the different models. ConclusionThe highly clustered multi-center data are used to construct the model and apply it to the prediction in a specific environment. RI and FEM can be selected for conditional prediction when the number of centers is small or the difference between centers is large due to different incidence rates. When the number of hearts is large and the sample size is large, RI can be selected for conditional prediction or RF for edge prediction.
The human skeletal muscle drives skeletal movement through contraction. Embedding its functional information into the human morphological framework and constructing a digital twin of skeletal muscle for simulating physical and physiological functions of skeletal muscle are of great significance for the study of "virtual physiological humans". Based on relevant literature both domestically and internationally, this paper firstly summarizes the technical framework for constructing skeletal muscle digital twins, and then provides a review from five aspects including skeletal muscle digital twins modeling technology, skeletal muscle data collection technology, simulation analysis technology, simulation platform and human medical image database. On this basis, it is pointed out that further research is needed in areas such as skeletal muscle model generalization, accuracy improvement, and model coupling. The methods and means of constructing skeletal muscle digital twins summarized in the paper are expected to provide reference for researchers in this field, and the development direction pointed out can serve as the next focus of research.
Debriefing has been identified as the most critical and important component in simulation-based education. Usually, debriefing following medical simulation is facilitated by a clinician (the debriefer). However, the shortage of clinical teachers due to the huge clinical workload has been the main obstacle for simulation-based medical education. Peer debriefing has been proved to be an effective alternative strategy to instructor-based debriefing, which might not be inferior to instructor-based debriefing. This review summarizes the application of peer debriefing in simulation-based medical education, and provides useful information for future practice in healthcare simulation.
ObjectiveTo explore the method of constructing time-dependent variables of clinical prognostic model, and to combine marginal structure model with clinical prognostic model to provide a more accurate tool for individualized prognostic assessment of patients. MethodsThrough data simulation, a training dataset with sample size of 7 000 and a validation dataset with sample size of 3 000 were constructed, and the predictive efficacy of ignoring treatment model, baseline no-treatment model, baseline treatment prediction model and marginal structure prediction model were respectively compared under different follow-up times and different situations. ResultsAt 2 follow-up time points, there was no significant difference between the marginal structure prediction model and the baseline treatment prediction model, but they were higher than the neglected treatment model and the baseline no treatment model. At 5 follow-up time points, the prediction ability of the marginal structure prediction model was significantly higher than that of the other three prediction models. ConclusionIn the case of time-dependent treatment in the observational cohort, the change of treatment after baseline should be considered when constructing the clinical prognosis model, otherwise the prediction accuracy of the prognosis model will be reduced.