New Paradigm of Identifiable General-response Cognitive Diagnostic Models: Beyond Categorical Data.

Cognitive diagnostic models (CDMs) are a popular family of discrete latent variable models that model students' mastery or deficiency of multiple fine-grained skills. CDMs have been most widely used to model categorical item response data such as binary or polytomous responses. With advances in technology and the emergence of varying test formats in modern educational assessments, new response types, including continuous responses such as response times, and count-valued responses from tests with repetitive tasks or eye-tracking sensors, have also become available. Variants of CDMs have been proposed recently for modeling such responses. However, whether these extended CDMs are identifiable and estimable is entirely unknown. We propose a very general cognitive diagnostic modeling framework for arbitrary types of multivariate responses with minimal assumptions, and establish identifiability in this general setting. Surprisingly, we prove that our general-response CDMs are identifiable under Q -matrix-based conditions similar to those for traditional categorical-response CDMs. Our conclusions set up a new paradigm of identifiable general-response CDMs. We propose an EM algorithm to efficiently estimate a broad class of exponential family-based general-response CDMs. We conduct simulation studies under various response types. The simulation results not only corroborate our identifiability theory, but also demonstrate the superior empirical performance of our estimation algorithms. We illustrate our methodology by applying it to a TIMSS 2019 response time dataset.

Spatial enrichment and genomic analyses reveal the link of NOMO1 with amyotrophic lateral sclerosis.

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. Spatial architecture of cell types and gene expression is the basis of cell-cell interactions, biological function and disease pathology, but is not well investigated in human motor cortex, a key ALS relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, it remains largely unclear what is the brain regional vulnerability of ALS-associated genes, and what is the genetic link between region-specific genes and ALS risk. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics (ST). We benchmarked SpatialE against another enrichment tool (Multimodal Intersection Analysis, MIA) using ST data from both human and mouse brain tissues. To investigate regional vulnerability, we analyzed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function (LOF) variants detected by whole-genome sequencing in ALS patients and controls, and then analyzed differential gene expression in the TargetALS RNA-seq dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than MIA in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogenous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 (L5) motor cortex, with abundant expression of upper motor neurons and L5 excitatory neurons. Burden analyses of rare LOF variants in L5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6,814 ALS patients and 3,324 controls (P = 0.029). Gene expression analyses in central nervous system tissues revealed down-regulation of NOMO1 in ALS, which is consistent with a LOF disease mechanism. In conclusion, our integrated ST and genomic analyses identified regional brain vulnerability in ALS and the association of a L5 gene (NOMO1) with ALS risk.

Simulation-based medical education in Canadian anesthesiology academic institutions: a national survey.

PURPOSE: Simulation-based medical education (SBME) is provided by all anesthesiology residency programs in Canada. The purpose of this study was to characterize SBME in Canadian anesthesiology residency training programs. METHODS: We administered a 21-question survey to the simulation director/coordinator for all 17 Canadian academic departments of anesthesiology from October 2019 to January 2020. The survey consisted of questions pertaining to the characteristics of the simulation centres, their faculty, learners, curriculum, and assessment processes. RESULTS: All 17 residency training programs participated in the survey and reported large variability in the number and formal training of simulation faculty and in content delivery. Five programs (29%) did not provide faculty recognition for curriculum design and running simulation sessions. Most programs offered one to four simulation sessions per academic year for each year of residency. All programs offered mannequin-based and part-task trainers for teaching technical and nontechnical skills. Fourteen programs (82%) offered interprofessional and interdisciplinary simulation sessions, and ten programs (59%) did not include in situ simulation training. Commonly reported barriers to faculty involvement were lack of protected time (12 programs, 71%), lack of financial compensation (ten programs, 59%), and lack of appreciation for SBME (seven programs, 41%). CONCLUSION: Large variability exists in the delivery of SBME in Canadian anesthesiology residency simulation programs, in part because of differences in financial/human resources and educational content. Future studies should explore whether training and patient outcomes differ between SBME programs and, if so, whether additional standardization is warranted.

RéSUMé: OBJECTIF: La formation médicale par simulation est offerte par tous les programmes de résidence en anesthésiologie au Canada. L'objectif de cette étude était de déterminer l'état actuel de la formation médicale par simulation dans les programmes canadiens de résidence en anesthésiologie. MéTHODE: D'octobre 2019 à janvier 2020, nous avons administré un sondage comportant 21 questions aux directions et équipes de coordination de la simulation des 17 départements universitaires d'anesthésiologie canadiens. L'enquête comportait des questions portant sur les caractéristiques des centres de simulation, le corps professoral, les apprenants et apprenantes, le programme d'études et les processus d'évaluation. RéSULTATS: Les 17 programmes de résidence ont tous participé à l'enquête et ont fait état d'une grande variabilité dans le nombre et la formation officielle du corps professoral en simulation ainsi que dans la prestation de contenu. Cinq programmes (29 %) n'ont pas reconnu le corps professoral en charge de la conception des programmes d'études et de l'organisation des séances de simulation. La plupart des programmes offraient une à quatre séances de simulation par année universitaire à chaque année de résidence. Tous les programmes disposaient de simulateurs d'entraînement pour tâches partielles et de mannequins pour enseigner des compétences techniques et non techniques. Quatorze programmes (82 %) offraient des séances de simulation interprofessionnelles et interdisciplinaires, et dix programmes (59 %) ne comportaient pas de formation par simulation in situ. Les obstacles les plus fréquemment signalés à la participation du corps professoral étaient le manque de temps protégé (12 programmes, 71 %), le manque de compensation financière (dix programmes, 59 %) et le manque d'appréciation de la formation médicale par simulation (sept programmes, 41 %). CONCLUSION: Il existe une grande variabilité dans la prestation de formation médicale par simulation dans les programmes de simulation pendant la résidence en anesthésiologie au Canada, causée en partie par des différences dans les ressources financières et humaines et par le contenu de la formation. Des études futures devraient déterminer si la formation et les issues pour les patient·es diffèrent d'un programme de formation médicale par simulation à l'autre et, dans l'affirmative, si une normalisation supplémentaire est justifiée.

A Spectral Method for Identifiable Grade of Membership Analysis with Binary Responses.

Grade of membership (GoM) models are popular individual-level mixture models for multivariate categorical data. GoM allows each subject to have mixed memberships in multiple extreme latent profiles. Therefore, GoM models have a richer modeling capacity than latent class models that restrict each subject to belong to a single profile. The flexibility of GoM comes at the cost of more challenging identifiability and estimation problems. In this work, we propose a singular value decomposition (SVD)-based spectral approach to GoM analysis with multivariate binary responses. Our approach hinges on the observation that the expectation of the data matrix has a low-rank decomposition under a GoM model. For identifiability, we develop sufficient and almost necessary conditions for a notion of expectation identifiability. For estimation, we extract only a few leading singular vectors of the observed data matrix and exploit the simplex geometry of these vectors to estimate the mixed membership scores and other parameters. We also establish the consistency of our estimator in the double-asymptotic regime where both the number of subjects and the number of items grow to infinity. Our spectral method has a huge computational advantage over Bayesian or likelihood-based methods and is scalable to large-scale and high-dimensional data. Extensive simulation studies demonstrate the superior efficiency and accuracy of our method. We also illustrate our method by applying it to a personality test dataset.

Age and its impact on crisis management performance and learning after simulation-based education by acute care physicians: a multicentre prospective cohort study.

BACKGROUND: Physiological changes associated with ageing could negatively impact the crisis resource management skills of acute care physicians. This study was designed to determine whether physician age impacts crisis resource management skills, and crisis resource management skills learning and retention using full-body manikin simulation training in acute care physicians. METHODS: Acute care physicians at two Canadian universities participated in three 8-min simulated crisis (pulseless electrical activity) scenarios. An initial crisis scenario (pre-test) was followed by debriefing with a trained facilitator and a second crisis scenario (immediate post-test). Participants returned for a third crisis scenario 3-6 months later (retention post-test). RESULTS: For the 48 participants included in the final analysis, age negatively correlated with baseline Global Rating Scale (GRS; r=-0.30, P<0.05) and technical checklist scores (r=-0.44, P<0.01). However, only years in practice and prior simulation experience, but not age, were significant in a subsequent stepwise regression analysis. Learning from simulation-based education was shown with a mean difference in scores from pre-test to immediate post-test of 2.28 for GRS score (P<0.001) and 1.69 for technical checklist correct score (P<0.001); learning was retained for 3-6 months. Only prior simulation experience was significantly correlated with a decreased change in learning (r=-0.30, P<0.05). CONCLUSIONS: A reduced amount of prior simulation training and increased years in practice, but not age on its own, were significant predictors of low baseline crisis resource management performance. Simulation-based education leads to crisis resource management learning that is well retained for 3-6 months, regardless of age or years in practice.

Going Deep in Diagnostic Modeling: Deep Cognitive Diagnostic Models (DeepCDMs).

Cognitive diagnostic models (CDMs) are discrete latent variable models popular in educational and psychological measurement. In this work, motivated by the advantages of deep generative modeling and by identifiability considerations, we propose a new family of DeepCDMs, to hunt for deep discrete diagnostic information. The new class of models enjoys nice properties of identifiability, parsimony, and interpretability. Mathematically, DeepCDMs are entirely identifiable, including even fully exploratory settings and allowing to uniquely identify the parameters and discrete loading structures (the "[Formula: see text]-matrices") at all different depths in the generative model. Statistically, DeepCDMs are parsimonious, because they can use a relatively small number of parameters to expressively model data thanks to the depth. Practically, DeepCDMs are interpretable, because the shrinking-ladder-shaped deep architecture can capture cognitive concepts and provide multi-granularity skill diagnoses from coarse to fine grained and from high level to detailed. For identifiability, we establish transparent identifiability conditions for various DeepCDMs. Our conditions impose intuitive constraints on the structures of the multiple [Formula: see text]-matrices and inspire a generative graph with increasingly smaller latent layers when going deeper. For estimation and computation, we focus on the confirmatory setting with known [Formula: see text]-matrices and develop Bayesian formulations and efficient Gibbs sampling algorithms. Simulation studies and an application to the TIMSS 2019 math assessment data demonstrate the usefulness of the proposed methodology.

A Joint MLE Approach to Large-Scale Structured Latent Attribute Analysis.

Structured Latent Attribute Models (SLAMs) are a family of discrete latent variable models widely used in education, psychology, and epidemiology to model multivariate categorical data. A SLAM assumes that multiple discrete latent attributes explain the dependence of observed variables in a highly structured fashion. Usually, the maximum marginal likelihood estimation approach is adopted for SLAMs, treating the latent attributes as random effects. The increasing scope of modern assessment data involves large numbers of observed variables and high-dimensional latent attributes. This poses challenges to classical estimation methods and requires new methodology and understanding of latent variable modeling. Motivated by this, we consider the joint maximum likelihood estimation (MLE) approach to SLAMs, treating latent attributes as fixed unknown parameters. We investigate estimability, consistency, and computation in the regime where sample size, number of variables, and number of latent attributes all can diverge. We establish the statistical consistency of the joint MLE and propose efficient algorithms that scale well to large-scale data for several popular SLAMs. Simulation studies demonstrate the superior empirical performance of the proposed methods. An application to real data from an international educational assessment gives interpretable findings of cognitive diagnosis.

Study on the effect of landfill gas on aerobic municipal solid waste degradation: Lab-scale model and tests.

Aeration is of great importance in landfill remediation. However, most existing studies on aerobic waste degradation ignore the presence of landfill gases. In this study, gas characteristics during aerobic waste degradation in the presence of landfill gas in lab-scale lysimeters were investigated. Oxygen (O2) was intermittently injected into municipal solid waste. Changes in the gas concentration and reaction rate of methane (CH4), carbon dioxide (CO2), and O2 during the reaction process were monitored and calculated. The results showed that all reactions, including aerobic degradation, CH4 oxidation, and anaerobic waste degradation, occurred simultaneously during landfill aeration. The maximum O2 consumption rate was 0.013 mol day-1 kg-1 dry waste. CH4 production was stimulated after the O2 content was insufficient to sustain the aerobic environment. Higher CH4 production was likely attributed to the remaining substrate and biomass from dead aerobic microorganisms decomposed by growing anaerobic microorganisms. Based on the biochemical reaction and principle of mass conservation, a gas balance model during waste aeration was established to analyze the proportions of aerobic waste degradation, CH4 oxidation, and anaerobic waste degradation. The CH4 oxidation reaction was more advantageous than the aerobic waste degradation reaction during aeration. With an increase in gas injection times, the anaerobic reaction gradually weakened. The maximum proportion of CH4 oxidation reaction could achieve at 21.4 % during aeration, which is of great significance for the waste degradation reaction. The maximum proportion of aerobic waste degradation and the minimum proportion of anaerobic waste degradation were approximately 16.0 % and 74.2 %, respectively. The results show that landfill gas should be considered in the progress of landfill aeration. This study provides a novel approach for calculating the proportion of reactions during landfill aeration, which deepens the understanding of the reaction process and contributes to the design of aerobic landfill projects.

A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses.

Latent class models are powerful statistical modeling tools widely used in psychological, behavioral, and social sciences. In the modern era of data science, researchers often have access to response data collected from large-scale surveys or assessments, featuring many items (large J) and many subjects (large N). This is in contrary to the traditional regime with fixed J and large N. To analyze such large-scale data, it is important to develop methods that are both computationally efficient and theoretically valid. In terms of computation, the conventional EM algorithm for latent class models tends to have a slow algorithmic convergence rate for large-scale data and may converge to some local optima instead of the maximum likelihood estimator (MLE). Motivated by this, we introduce the tensor decomposition perspective into latent class analysis with binary responses. Methodologically, we propose to use a moment-based tensor power method in the first step and then use the obtained estimates as initialization for the EM algorithm in the second step. Theoretically, we establish the clustering consistency of the MLE in assigning subjects into latent classes when N and J both go to infinity. Simulation studies suggest that the proposed tensor-EM pipeline enjoys both good accuracy and computational efficiency for large-scale data with binary responses. We also apply the proposed method to an educational assessment dataset as an illustration.

Generic Identifiability of the DINA Model and Blessing of Latent Dependence.

Cognitive diagnostic models are a powerful family of fine-grained discrete latent variable models in psychometrics. Within this family, the DINA model is a fundamental and parsimonious one that has received significant attention. Similar to other complex latent variable models, identifiability is an important issue for CDMs, including the DINA model. Gu and Xu (Psychometrika 84(2):468-483, 2019) established the necessary and sufficient conditions for strict identifiability of the DINA model. Despite being the strongest possible notion of identifiability, strict identifiability may impose overly stringent requirements on designing the cognitive diagnostic tests. This work studies a slightly weaker yet very useful notion, generic identifiability, which means parameters are identifiable almost everywhere in the parameter space, excluding only a negligible subset of measure zero. We propose transparent generic identifiability conditions for the DINA model, relaxing existing conditions in nontrivial ways. Under generic identifiability, we also explicitly characterize the forms of the measure-zero sets where identifiability breaks down. In addition, we reveal an interesting blessing-of-latent-dependence phenomenon under DINA-that is, dependence between the latent attributes can restore identifiability under some otherwise unidentifiable [Formula: see text]-matrix designs. The blessing of latent dependence provides useful practical implications and reassurance for real-world designs of cognitive diagnostic assessments.

Energy Dissipation and Dynamic Fragmentation of Alkali-Activated Rubber Mortar under Multi-Factor Coupling Effect.

Recycled rubber aggregate (RRA) made from ground tire rubber has been promoted for its light weight and shock resistance. The high alkalinity of alkali-activated slag mortar has a modification effect on the surface of RRA. This paper studies the performance of alkali-activated slag mortar using RRA as aggregate (RASM), which has significance for applications in low-carbon building materials. The orthogonal test analysis method was used to analyze the significance and correlation of the main variables of the test. The dynamic energy absorption capacity and crushing state of RASM under the synergistic effect of various factors were studied using the separating Hopkinson pressure bar (SHPB) test system. The energy absorption characteristics and failure modes of RASM were analyzed by SEM and microscopic pore characterization. The results show that the increase of the alkali equivalent of the mix ratio will increase the peak value of the absorption energy of the specimen. When the size of the RRA is between 0.48 mm~0.3 mm, the dynamic energy absorption of the specimen will reach its peak value. Although the increase in the total volume of RRA will reduce the energy absorption capacity of RASM specimens, its crack resistance is enhanced.

Evidence supporting a cultural evolutionary theory of prosocial religions in contemporary workplace safety data.

A prominent line of cultural evolutionary theory hypothesizes that religiously inspired prosocial behavior enhances the fecundity of pious groups, causing them to outcompete non-religious communities and spread their prosocial values. We present evidence concerning contemporary workplace safety, in the United States, that unexpectedly tested implications of this cultural evolutionary hypothesis. Avoiding workplace injury requires cooperation and injury influences fitness, thus cultural evolutionary theory would anticipate that religious communities should exhibit fewer workplace injuries. Indeed, we find that the proportion of a community adhering to a religion correlates negatively with rates of workplace injury in its private-sector establishments. This correlation emerges primarily when secular workplace safety authorities are not prominent, thus echoing evidence that religiously inspired prosocial behavior mainly occurs absent "earthly" sanctioning authorities. Furthermore, the percent of religiously affiliated individuals in a community correlates with safety investments, suggesting that workplace injury reductions in religious communities result from individually costly, group-benefitting cooperation.

A smart enzyme reactor based on a photo-responsive hydrogel for purifying water from phenol contaminated sources.

In this paper, a smart enzyme reactor (SER) was synthesized using immobilized tyrosinase (Tyr) in a photo-responsive hydrogel via a polydopamine-assisted self-assembly strategy for purifying water from phenol contaminated water. PDA was not only utilized as a binder between Tyr and the hydrogel to prevent the leakage of Tyr with relatively high enzymatic activity from the SER, but also acted as a light absorber to trigger the hydrophilic/hydrophobic switching of PNIPAm hydrogels to realize the efficient reclamation of clean water. Experimental results showed that the SER maintained a well-defined porous structure with excellent elasticity, which was beneficial for water transport and enzyme accessibility. And the stability and reusability of Tyr in the degradation of phenol were all improved. Furthermore, clean water could be reclaimed completely and facilely by light irradiation after enzymatic remediation in the SER.

Impact of aerosol box on intubation during COVID-19: a simulation study of normal and difficult airways.

PURPOSE: Patients with coronavirus disease (COVID-19) are at risk of requiring mechanical ventilation, and concerns of protecting healthcare workers during aerosol-generating medical procedures has led to the design of the aerosol box. METHODS: We conducted a randomized crossover mannequin-based simulation study to compare airway management with and without the aerosol box. Thirty-five anesthesiology participants and three critical care participants with more than 50 intubations with videolaryngoscopes were recruited. There were four airway simulations with and without the aerosol box (normal, pharyngeal swelling, cervical spine rigidity, and tongue edema). Each participant intubated the mannequin in eight consecutive simulations. The primary outcome of the study was time to intubation. Secondary outcomes included intubation attempts, optimization maneuvers, and personal protective equipment breaches. RESULTS: Mean (standard deviation [SD]) time to intubation overall with the box was 30.9 (23.0) sec, while the time to intubation without the box was 25.1 (12.2) sec (mean difference, 5.8; 95% confidence interval [CI], -2.9 to 14.5). For the normal airway scenario, the mean (SD) time to intubation was 18.6 (3.5) sec for no box and 20.4 (3.3) sec for box (mean difference, 1.8; 95% CI, 0.2 to 3.4). During difficult airway scenarios only, the time to intubation was 34.4 (25.6) sec with the aerosol box and 27.3 (13.2) sec without the aerosol box (mean difference, 7.1; 95% CI, -2.5 to 16.7). There were more intubation attempts, personal protective equipment breaches, and optimization maneuvers during use of the aerosol box. CONCLUSIONS: In this mannequin-based simulation study, the use of the aerosol box increased the time to intubation in some contexts but not others. Further studies in a clinical setting should be conducted to make appropriate modifications to the aerosol box to fully elicit its efficacy and safety prior to implementation in airway guidelines for managing patients with COVID-19.

RéSUMé: OBJECTIF: Les patients atteints de la maladie à coronavirus (COVID-19) courent le risque d'avoir besoin de ventilation mécanique, et les inquiétudes quant à la protection des travailleurs de la santé pendant les interventions médicales générant des aérosols ont motivé la conception d'une boîte pour contenir les aérosols. MéTHODE: Nous avons réalisé une étude de simulation croisée randomisée sur des mannequins afin de comparer la prise en charge des voies aériennes avec et sans boîte pour contenir les aérosols. Trente-cinq anesthésiologistes et trois intensivistes ayant pratiqué plus de 50 intubations avec des vidéolaryngoscopes ont été recrutés. Quatre simulations de voies aériennes avec et sans boîte pour contenir les aérosols ont été évaluées (voies aériennes normales, œdème pharyngé, rigidité de la colonne cervicale et œdème de la langue). Chaque participant a intubé le mannequin dans huit simulations consécutives. Le critère d'évaluation principal de l'étude était le temps nécessaire à l'intubation. Les critères secondaires comprenaient le nombre de tentatives d'intubation, les manœuvres d'optimisation et les bris de stérilité des équipements de protection individuelle. RéSULTATS: Globalement, le temps moyen (écart type [ÉT]) d'intubation avec la boîte était de 30,9 (23,0) sec, alors que le temps d'intubation sans la boîte était de 25,1 (12,2) sec (différence moyenne, 5,8; intervalle de confiance [IC] 95 %, -2,9 à 14,5). Dans la mise en situation simulant des voies aériennes normales, le temps moyen (ÉT) d'intubation était de 18,6 (3,5) sec sans la boîte et 20,4 (3,3) sec avec la boîte (différence moyenne, 1,8; IC 95 %, 0,2 à 3,4). Dans la mise en situation simulant des voies aériennes difficiles seulement, le temps d'intubation était de 34,4 (25,6) sec avec la boîte à aérosol et 27,3 (13,2) sec sans la boîte (différence moyenne, 7,1; IC 95 %, -2,5 à 16,7). Lors de l'utilisation de la boîte pour contenir les aérosols, les tentatives d'intubation étaient plus nombreuses, tout comme les bris de stérilité des équipements de protection individuelle et le nombre de manœuvres d'optimisation. CONCLUSION: Dans cette étude de simulation sur mannequin, l'utilisation de la boîte pour contenir les aérosols a augmenté le temps nécessaire à l'intubation dans certains contextes mais pas dans d'autres. Des études supplémentaires devraient être réalisées dans un cadre clinique pour apporter des modifications adaptées à la boîte pour contenir les aérosols afin d'optimiser son efficacité et la sécurité qu'elle procure avant de l'ajouter aux recommandations de prise en charge des voies aériennes de patients atteints de la COVID-19.

The Sufficient and Necessary Condition for the Identifiability and Estimability of the DINA Model.

Cognitive diagnosis models (CDMs) are useful statistical tools in cognitive diagnosis assessment. However, as many other latent variable models, the CDMs often suffer from the non-identifiability issue. This work gives the sufficient and necessary condition for identifiability of the basic DINA model, which not only addresses the open problem in Xu and Zhang (Psychometrika 81:625-649, 2016) on the minimal requirement for identifiability, but also sheds light on the study of more general CDMs, which often cover DINA as a submodel. Moreover, we show the identifiability condition ensures the consistent estimation of the model parameters. From a practical perspective, the identifiability condition only depends on the Q-matrix structure and is easy to verify, which would provide a guideline for designing statistically valid and estimable cognitive diagnosis tests.

Hypothesis Testing of the Q-matrix.

The recent surge of interests in cognitive assessment has led to the development of cognitive diagnosis models. Central to many such models is a specification of the Q-matrix, which relates items to latent attributes that have natural interpretations. In practice, the Q-matrix is usually constructed subjectively by the test designers. This could lead to misspecification, which could result in lack of fit of the underlying statistical model. To test possible misspecification of the Q-matrix, traditional goodness of fit tests, such as the Chi-square test and the likelihood ratio test, may not be applied straightforwardly due to the large number of possible response patterns. To address this problem, this paper proposes a new statistical method to test the goodness fit of the Q-matrix, by constructing test statistics that measure the consistency between a provisional Q-matrix and the observed data for a general family of cognitive diagnosis models. Limiting distributions of the test statistics are derived under the null hypothesis that can be used for obtaining the test p-values. Simulation studies as well as a real data example are presented to demonstrate the usefulness of the proposed method.

Does the age of acute care physicians impact their (1) crisis management performance and (2) learning after simulation-based education? A protocol for a multicentre prospective cohort study in Toronto and Ottawa, Canada.

INTRODUCTION: The proportion of older acute care physicians (ACPs) has been steadily increasing. Ageing is associated with physiological changes and prospective research investigating how such age-related physiological changes affect clinical performance, including crisis resource management (CRM) skills, is lacking. There is a gap in the literature on whether physician's age influences baseline CRM performance and also learning from simulation. We aim to investigate whether ageing is associated with baseline CRM skills of ACPs (emergency, critical care and anaesthesia) using simulated crisis scenarios and to assess whether ageing influences learning from simulation-based education. METHODS AND ANALYSIS: This is a prospective cohort multicentre study recruiting ACPs from the Universities of Toronto and Ottawa, Canada. Each participant will manage an advanced cardiovascular life support crisis-simulated scenario (pretest) and then be debriefed on their CRM skills. They will then manage another simulated crisis scenario (immediate post-test). Three months after, participants will return to manage a third simulated crisis scenario (retention post-test). The relationship between biological age and chronological age will be assessed by measuring the participants CRM skills and their ability to learn from high-fidelity simulation. ETHICS AND DISSEMINATION: This protocol was approved by Sunnybrook Health Sciences Centre Research Ethics Board (REB Number 140-2015) and the Ottawa Health Science Network Research Ethics Board (#20150173-01H). The results will be disseminated in a peer-reviewed journal and at scientific meetings. TRIAL REGISTRATION NUMBER: NCT02683447; Pre-results.

The effect of simulator fidelity on acquiring non-technical skills: a randomized non-inferiority trial.

INTRODUCTION: As simulator fidelity (i.e., realism) increases from low to high, the simulator more closely resembles the real environment, but it also becomes more expensive. It is generally assumed that the use of high-fidelity simulators results in better learning; however, the effect of fidelity on learning non-technical skills (NTS) is unknown. This was a non-inferiority trial comparing the efficacy of high- vs low-fidelity simulators on learning NTS. METHODS: Thirty-six postgraduate medical trainees were recruited for the trial. During the pre-test phase, the trainees were randomly assigned to manage a scenario using either a high-fidelity simulator (HFS) or a low-fidelity simulator (LFS), followed by expert debriefing. All trainees then underwent a video recorded post-test scenario on a HFS, and the NTS were assessed between the two groups. The primary outcome was the overall post-test Ottawa Global Rating Scale (OGRS), while controlling for overall pre-test OGRS scores. Non-inferiority between the LFS and HFS was based on a non-inferiority margin of greater than 1. RESULTS: For our primary outcome, the mean (SD) post-test overall OGRS score was not significantly different between the HFS and LFS groups after controlling for pre-test overall OGRS scores [3.8 (0.9) vs 4.0 (0.9), respectively; mean difference, 0.2; 95% confidence interval, -0.4 to 0.8; P = 0.48]. For our secondary outcomes, the post-test total OGRS score was not significantly different between the HFS and LFS groups after controlling for pre-test total OGRS scores (P = 0.33). There were significant improvements in mean overall (P = 0.01) and total (P = 0.003) OGRS scores from pre-test to post-test. There were no significant associations between postgraduate year (P = 0.82) and specialty (P = 0.67) on overall OGRS performance. CONCLUSION: This study suggests that low-fidelity simulators are non-inferior to the more costly high-fidelity simulators for teaching NTS to postgraduate medical trainees.

Efficacy of Surgical Simulation Training in a Low-Income Country.

INTRODUCTION: Simulation training has evolved as an important component of postgraduate surgical education and has shown to be effective in teaching procedural skills. Despite potential benefits to low- and middle-income countries (LMIC), simulation training is predominately used in high-income settings. This study evaluates the effectiveness of simulation training in one LMIC (Rwanda). METHODS: Twenty-six postgraduate surgical trainees at the University of Rwanda (Kigali, Rwanda) and Dalhousie University (Halifax, Canada) participated in the study. Participants attended one 3-hour simulation session using a high-fidelity, tissue-based model simulating the creation of an end ileostomy. Each participant was anonymously recorded completing the assigned task at three time points: prior to, immediately following, and 90 days following the simulation training. A single blinded expert reviewer assessed the performance using the Objective Structured Assessment of Technical Skill (OSATS) instrument. RESULTS: The mean OSATS score improvement for participants who completed all the assessments was 6.1 points [95 % Confidence Interval (CI) 2.2-9.9, p = 0.005]. Improvement was sustained over a 90-day period with a mean improvement of 4.1 points between the first and third attempts (95 % CI 0.3-7.9, p = 0.038). Simulation training was effective in both study sites, though most gains occurred with junior-level learners, with a mean improvement of 8.3 points (95 % CI 5.1-11.6, p < 0.001). Significant improvements were not identified for senior-level learners. CONCLUSION: This study supports the benefit for simulation in surgical training in LMICs. Skill improvements were limited to junior-level trainees. This work provides justification for investment in simulation-based curricula in Rwanda and potentially other LMICs.