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IntroductionDuring flow diversion, the choice of the length, diameter, and location of the deployed stent are critical for the success of the procedure. Sim&Size software, based on the three-dimensional rotational angiography (3D-RA) acquisition, simulates the release of the stent, suggesting optimal sizing, and displaying the degree of the wall apposition.ObjectiveTo demonstrate technical and clinical impacts of the Sim&Size simulation during treatment with the Pipeline Flex Embolization Device.MethodsConsecutive patients who underwent aneurysm embolization with Pipeline at our department were retrospectively enrolled (January 2015–December 2017) and divided into two groups: treated with and without simulation. Through univariate and multivariate models, we evaluated: (1) rate of corrective intervention for non-optimal stent placement, (2) duration of intervention, (3) radiation dose, and (4) stent length.Results189 patients, 95 (50.2%) without and 94 (49.7%) with software assistance were analyzed. Age, sex, comorbidities, aneurysm characteristics, and operator’s experience were comparable among the two groups. Procedures performed with the software had a lower rate of corrective intervention (9% vs 20%, p=0.036), a shorter intervention duration (46 min vs 52 min, p=0.002), a lower median radiation dose (1150 mGy vs 1558 mGy, p<0.001), and a shorter stent length (14 mm vs 16 mm, p<0.001).ConclusionsIn our experience, the use of the virtual simulation during Pipeline treatment significantly reduced the need for corrective intervention, the procedural time, the radiation dose, and the length of the stent.

Virtual patients are interactive digital simulations of clinical scenarios for the purpose of health professions education. There is no current collated evidence on the effectiveness of this form of education. The goal of this study was to evaluate the effectiveness of virtual patients compared with traditional education, blended with traditional education, compared with other types of digital education, and design variants of virtual patients in health professions education. The outcomes of interest were knowledge, skills, attitudes, and satisfaction. We performed a systematic review on the effectiveness of virtual patient simulations in pre- and postregistration health professions education following Cochrane methodology. We searched 7 databases from the year 1990 up to September 2018. No language restrictions were applied. We included randomized controlled trials and cluster randomized trials. We independently selected studies, extracted data, and assessed risk of bias and then compared the information in pairs. We contacted study authors for additional information if necessary. All pooled analyses were based on random-effects models. A total of 51 trials involving 4696 participants met our inclusion criteria. Furthermore, 25 studies compared virtual patients with traditional education, 11 studies investigated virtual patients as blended learning, 5 studies compared virtual patients with different forms of digital education, and 10 studies compared different design variants. The pooled analysis of studies comparing the effect of virtual patients to traditional education showed similar results for knowledge (standardized mean difference [SMD]=0.11, 95% CI -0.17 to 0.39, I =74%, n=927) and favored virtual patients for skills (SMD=0.90, 95% CI 0.49 to 1.32, I =88%, n=897). Studies measuring attitudes and satisfaction predominantly used surveys with item-by-item comparison. Trials comparing virtual patients with different forms of digital education and design variants were not numerous enough to give clear recommendations. Several methodological limitations in the included studies and heterogeneity contributed to a generally low quality of evidence. Low to modest and mixed evidence suggests that when compared with traditional education, virtual patients can more effectively improve skills, and at least as effectively improve knowledge. The skills that improved were clinical reasoning, procedural skills, and a mix of procedural and team skills. We found evidence of effectiveness in both high-income and low- and middle-income countries, demonstrating the global applicability of virtual patients. Further research should explore the utility of different design variants of virtual patients.

Computational modeling of behavior has revolutionized psychology and neuroscience. By fitting models to experimental data we can probe the algorithms underlying behavior, find neural correlates of computational variables and better understand the effects of drugs, illness and interventions. But with great power comes great responsibility. Here, we offer ten simple rules to ensure that computational modeling is used with care and yields meaningful insights. In particular, we present a beginner-friendly, pragmatic and details-oriented introduction on how to relate models to data. What, exactly, can a model tell us about the mind? To answer this, we apply our rules to the simplest modeling techniques most accessible to beginning modelers and illustrate them with examples and code available online. However, most rules apply to more advanced techniques. Our hope is that by following our guidelines, researchers will avoid many pitfalls and unleash the power of computational modeling on their own data.

Differential abundance analysis is controversial throughout microbiome research. Gold standard approaches require laborious measurements of total microbial load, or absolute number of microorganisms, to accurately determine taxonomic shifts. Therefore, most studies rely on relative abundance data. Here, we demonstrate common pitfalls in comparing relative abundance across samples and identify two solutions that reveal microbial changes without the need to estimate total microbial load. We define the notion of “reference frames”, which provide deep intuition about the compositional nature of microbiome data. In an oral time series experiment, reference frames alleviate false positives and produce consistent results on both raw and cell-count normalized data. Furthermore, reference frames identify consistent, differentially abundant microbes previously undetected in two independent published datasets from subjects with atopic dermatitis. These methods allow reassessment of published relative abundance data to reveal reproducible microbial changes from standard sequencing output without the need for new assays. Most microbiome studies make conclusions based on changes in relative abundance of taxa, inferred from sequencing data. Here, the authors highlight common pitfalls in comparing relative abundance across samples, and identify solutions that reveal microbial changes without the need to estimate total microbial load.

ABSTRACT OBJECTIVES Evaluating the patient impact of health professions education is a societal priority with many challenges. Researchers would benefit from a summary of topics studied and potential methodological problems. We sought to summarize key information on patient outcomes identified in a comprehensive systematic review of simulation-based instruction. DATA SOURCES Systematic search of MEDLINE, EMBASE, CINAHL, PsychINFO, Scopus, key journals, and bibliographies of previous reviews through May 2011. STUDY ELIGIBILITY Original research in any language measuring the direct effects on patients of simulation-based instruction for health professionals, in comparison with no intervention or other instruction. APPRAISAL and SYNTHESIS Two reviewers independently abstracted information on learners, topics, study quality including unit of analysis, and validity evidence. We pooled outcomes using random effects. RESULTS From 10,903 articles screened, we identified 50 studies reporting patient outcomes for at least 3,221 trainees and 16,742 patients. Clinical topics included airway management (14 studies), gastrointestinal endoscopy (12), and central venous catheter insertion (8). There were 31 studies involving postgraduate physicians and seven studies each involving practicing physicians, nurses, and emergency medicine technicians. Fourteen studies (28 %) used an appropriate unit of analysis. Measurement validity was supported in seven studies reporting content evidence, three reporting internal structure, and three reporting relations with other variables. The pooled Hedges’ g effect size for 33 comparisons with no intervention was 0.47 (95 % confidence interval [CI], 0.31–0.63); and for nine comparisons with non-simulation instruction, it was 0.36 (95 % CI, −0.06 to 0.78). LIMITATIONS Focused field in education; high inconsistency (I 2  > 50 % in most analyses). CONCLUSIONS Simulation-based education was associated with small-moderate patient benefits in comparison with no intervention and non-simulation instruction, although the latter did not reach statistical significance. Unit of analysis errors were common, and validity evidence was infrequently reported.

A binary phylogenetic network may or may not be obtainable from a tree by the addition of directed edges (arcs) between tree arcs. Here, we establish a precise and easily tested criterion (based on \"2-SAT\") that efficiently determines whether or not any given network can be realized in this way. Moreover, the proof provides a polynomial-time algorithm for finding one or more trees (when they exist) on which the network can be based. A number of interesting consequences are presented as corollaries; these lead to some further relevant questions and observations, which we outline in the conclusion.

In silico clinical trials (ISCTs) are an emerging method in modeling and simulation where medical interventions are evaluated using computational models of patients. ISCTs have the potential to provide cost-effective, time-efficient, and ethically favorable alternatives for evaluating the safety and effectiveness of medical devices. However, ensuring the credibility of ISCT results is a significant challenge. This paper aims to identify unique considerations for assessing the credibility of ISCTs and proposes an ISCT credibility assessment workflow based on recently published model assessment frameworks. First, we review various ISCTs described in the literature, carefully selected to showcase the range of methodological options available. These studies cover a wide variety of devices, reasons for conducting ISCTs, patient model generation approaches including subject-specific versus ‘synthetic’ virtual patients, complexity levels of devices and patient models, incorporation of clinician or clinical outcome models, and methods for integrating ISCT results with real-world clinical trials. We next discuss how verification, validation, and uncertainty quantification apply to ISCTs, considering the range of ISCT approaches identified. Based on our analysis, we then present a hierarchical workflow for assessing ISCT credibility, using a general credibility assessment framework recently published by the FDA’s Center for Devices and Radiological Health. Overall, this work aims to promote standardization in ISCTs and contribute to the wider adoption and acceptance of ISCTs as a reliable tool for evaluating medical devices.

Classical simulation of quantum computation is necessary for studying the numerical behavior of quantum algorithms, as there does not yet exist a large viable quantum computer on which to perform numerical tests. Tensor network (TN) contraction is an algorithmic method that can efficiently simulate some quantum circuits, often greatly reducing the computational cost over methods that simulate the full Hilbert space. In this study we implement a tensor network contraction program for simulating quantum circuits using multi-core compute nodes. We show simulation results for the Max-Cut problem on 3- through 7-regular graphs using the quantum approximate optimization algorithm (QAOA), successfully simulating up to 100 qubits. We test two different methods for generating the ordering of tensor index contractions: one is based on the tree decomposition of the line graph, while the other generates ordering using a straight-forward stochastic scheme. Through studying instances of QAOA circuits, we show the expected result that as the treewidth of the quantum circuit's line graph decreases, TN contraction becomes significantly more efficient than simulating the whole Hilbert space. The results in this work suggest that tensor contraction methods are superior only when simulating Max-Cut/QAOA with graphs of regularities approximately five and below. Insight into this point of equal computational cost helps one determine which simulation method will be more efficient for a given quantum circuit. The stochastic contraction method outperforms the line graph based method only when the time to calculate a reasonable tree decomposition is prohibitively expensive. Finally, we release our software package, qTorch (Quantum TensOR Contraction Handler), intended for general quantum circuit simulation. For a nontrivial subset of these quantum circuits, 50 to 100 qubits can easily be simulated on a single compute node.

To evaluate model fit in confirmatory factor analysis, researchers compare goodness-of-fit indices (GOFs) against fixed cutoff values (e.g., CFI > .950) derived from simulation studies. Methodologists have cautioned that cutoffs for GOFs are only valid for settings similar to the simulation scenarios from which cutoffs originated. Despite these warnings, fixed cutoffs for popular GOFs (i.e., χ 2 , χ 2 / df , CFI, RMSEA, SRMR) continue to be widely used in applied research. We (1) argue that the practice of using fixed cutoffs needs to be abandoned and (2) review time-honored and emerging alternatives to fixed cutoffs. We first present the most in-depth simulation study to date on the sensitivity of GOFs to model misspecification (i.e., misspecified factor dimensionality and unmodeled cross-loadings) and their susceptibility to further data and analysis characteristics (i.e., estimator, number of indicators, number and distribution of response options, loading magnitude, sample size, and factor correlation). We included all characteristics identified as influential in previous studies. Our simulation enabled us to replicate well-known influences on GOFs and establish hitherto unknown or underappreciated ones. In particular, the magnitude of the factor correlation turned out to moderate the effects of several characteristics on GOFs. Second, to address these problems, we discuss several strategies for assessing model fit that take the dependency of GOFs on the modeling context into account. We highlight tailored (or “dynamic”) cutoffs as a way forward. We provide convenient tables with scenario-specific cutoffs as well as regression formulae to predict cutoffs tailored to the empirical setting of interest.

Researchers and policymakers rely on computer simulations called integrated assessment models to determine the best strategies for tackling climate change. Here, scientists present opposing views on the suitability of these simulations. Opposing views on the suitability of integrated assessment models. Residential neighborhoods near the Interstate 10 sit in floodwater in the wake of Hurricane Harvey on August 29, 2017 Credit: Marcus Yam/Los Angeles Times/Getty