Confidence Measure Research Articles

ZygosityPredictor.

ZygosityPredictor provides functionality to evaluate how many copies of a gene are affected by mutations in next generation sequencing data. In cancer samples, the tool processes both somatic and germline mutations. In particular, ZygosityPredictor computes the number of affected copies for single nucleotide variants and small insertions and deletions (Indels). In addition, the tool integrates information at gene level via phasing of several variants and subsequent logic to derive how strongly a gene is affected by mutations and provides a measure of confidence. This information is of particular interest in precision oncology, e.g. when assessing whether unmutated copies of tumor-suppressor genes remain. ZygosityPredictor was implemented as an R-package and is available via Bioconductor at https://bioconductor.org/packages/ZygosityPredictor. Detailed documentation is provided in the vignette including application to an example genome.

Cultural identity, cultural confidence, and folk activities: An empirical study of the folk culture of Moon-cake Gambling at the Mid-Autumn Festival in Xiamen, China

This article aims to clarify the relationship between cultural identity, cultural confidence, and folk activities; to strengthen the role of folk activities in improving cultural identity and cultural confidence. Study participants were 454 people participating in Moon-cake Gambling at the Mid-Autumn Festival in Xiamen, China. The participants completed measures of personal information, involvement, perceived value, authenticity, cultural identity, and cultural confidence. Structural equation modelling indicated involvement to be associated with higher authenticity of folk activities, value perception, and cultural identity. Value perception and authenticity were associated with higher cultural identity and cultural confidence, which was also associated with higher cultural confidence. These results suggest creative transformation of the authenticity of folk activities to enhance the cultural identity and cultural confidence of local culture.

Confidence in mathematics is confounded by responses to reverse-coded items.

This study investigates the confounding effects of reverse-coded items on the measurement of confidence in mathematics using data from the 2019 Trends in International Mathematics and Science Study (TIMSS). The sample came from the Saudi Arabian cohort of 8th graders in 2019 involving 4,515 students. Through mixture modeling, two subgroups responding in similar ways to reverse-coded items were identified representing approximately 9% of the sample. Their response to positively valenced and negatively valenced items showed inconsistency and the observed unexpected response patterns were further verified using Lz*, U3, and the number of Guttman errors person fit indicators. Psychometric analyses on the full sample and the truncated sample after deleting the aberrant responders indicated significant improvements in both internal consistency reliability and factorial validity. It was concluded that reverse-coded items contribute to systematic measurement error that is associated with distorted item level parameters that compromised the scale's reliability and validity. The study underscores the need for reconsideration of reverse-coded items in survey design, particularly in contexts involving younger populations and low-achieving students.

Jumpiness in Ensemble Forecasts of Atlantic Tropical Cyclone Tracks

Abstract We investigate the run-to-run consistency (jumpiness) of ensemble forecasts of tropical cyclone tracks from three global centers: ECMWF, the Met Office, and NCEP. We use a divergence function to quantify the change in cross-track position between consecutive ensemble forecasts initialized at 12-h intervals. Results for the 2019–21 North Atlantic hurricane season show that the jumpiness varied substantially between cases and centers, with no common cause across the different ensemble systems. Recent upgrades to the Met Office and NCEP ensembles reduced their overall jumpiness to match that of the ECMWF ensemble. The average divergence over the set of cases provides an objective measure of the expected change in cross-track position from one forecast to the next. For example, a user should expect on average that the ensemble mean position will change by around 80–90 km in the cross-track direction between a forecast for 120 h ahead and the updated forecast made 12 h later for the same valid time. This quantitative information can support users’ decision-making, for example, in deciding whether to act now or wait for the next forecast. We did not find any link between jumpiness and skill, indicating that users should not rely on the consistency between successive forecasts as a measure of confidence. Instead, we suggest that users should use ensemble spread and probabilistic information to assess forecast uncertainty, and consider multimodel combinations to reduce the effects of jumpiness. Significance Statement Forecasting the tracks of tropical cyclones is essential to mitigate their impacts on society. Numerical weather prediction models provide valuable guidance, but occasionally there is a large jump in the predicted track from one run to the next. This jumpiness complicates the creation and communication of consistent forecast advisories and early warnings. In this work we aim to better understand forecast jumpiness and we provide practical information to forecasters to help them better use the model guidance. We show that the jumpiest cases are different for different modeling centers, that recent model upgrades have reduced forecast jumpiness, and that there is not a strong link between jumpiness and forecast skill.

Introduction to Treating Patients Exposed to Chemical, Biological, Radiological, and Nuclear (CBRN) Threats: A Military Medical Case-Based Curriculum.

Ensuring proficiency in responding to, evaluating, and treating chemical, biological, radiological, and nuclear (CBRN) casualties is a critical component of military medical student education. To meet this objective, we developed a case-based CBRN curriculum that can serve as a model to address potential curricular gaps for civilian prehospital, UME, and GME programs. The curriculum was administered in two sessions, 1 month apart, each with individual student preparation, including an optional asynchronous online module and a review of clinical practice guidelines. Session one consisted of a 2-hour introductory lecture, followed by a student reflection. Session two consisted of a 1-hour small-group case study, designed as a multimodal exercise with a corresponding computer-based worksheet and knowledge check. Forty-five teams consisting of three to four second-year medical students (N = 170) completed the sessions and course survey. Sixty-four percent of student teams were extremely or quite satisfied with what they learned, 62% found the materials very or quite relevant to their needs, and 69% rated the instructional materials as extremely or quite understandable. Student feedback included designating additional time for worksheet completion. A case-based training on CBRN patient care earned positive ratings for the clarity of instruction, the impact on students as learners, and the feasibility of the training. Future training evolutions could track student completion of prework, extend the allotted time for activity completion, and evaluate curricular effectiveness through pre-post measurement of students' confidence in their ability to care for a CBRN patient.

Uncertainty Estimation in Unsupervised MR-CT Synthesis of Scoliotic Spines

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Goal:</i> Uncertainty estimations through approximate Bayesian inference provide interesting insights to deep neural networks' behavior. In unsupervised learning tasks, where expert labels are unavailable, it becomes ever more important to critique the model through uncertainties. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Methods:</i> This paper presents a proof-of-concept for generalizing the aleatoric and epistemic uncertainties in unsupervised MR-CT synthesis of scoliotic spines. A novel adaptation of the cycle-consistency constraint in CycleGAN is proposed such that the model predicts the aleatoric uncertainty maps in addition to the standard volume-to-volume translation between Magnetic Resonance (MR) and Computed Tomography (CT) data. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Results:</i> Ablation experiments were performed to understand uncertainty estimation as an implicit regularizer and a measure of the model's confidence. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Conclusion:</i> The aleatoric uncertainty helps in distinguishing between the bone and soft-tissue regions in CT and MR data during translation, while the epistemic uncertainty provides interpretable information to the user for downstream tasks.

Multiscale Cross-Modal Homogeneity Enhancement and Confidence-Aware Fusion for Multispectral Pedestrian Detection

Multispectral pedestrian detection has shown many advantages in a variety of environments, particularly poor illumination conditions, by leveraging visible-thermal modalities. However, in-depth insight into distinguishing the complementary content of multimodal data and exploring the extent of multimodal feature fusion is still lacking. In this paper, we propose a novel multispectral pedestrian detector with multiscale cross-modal homogeneity enhancement and confidence-aware feature fusion. RGB and thermal streams are constructed to extract features and generate candidate proposals. During feature extraction, multiscale cross-modal homogeneity enhancement is proposed to enhance single-modal features using the separated homogeneous features via modal interactions. Homogeneity features encode the semantic information of the scene and are extracted from the RGB-thermal pairs by employing a channel attention mechanism. Proposals from two modalities are united to obtain multimodal proposals. Then, confidence measurement fusion is proposed to achieve multispectral feature fusion in each proposal by measuring the internal confidence of each modality and the interaction confidence between modalities. In addition, a confidence transfer loss function is designed to focus more on hard-to-detect samples during training. Experimental results on two challenging datasets demonstrate that the proposed method achieves better performance compared to existing methods.

Confidence Building Measures Among East African Community for Strengthening Peace in the Region

The East Africa Community (EAC) comprises various instabilities, including violence, humanitarian crises, environmental issues, and conflicts, both within and between states. In response, these countries have established defense diplomacy, which involves the non-violent utilization of military resources to pursue peace and stability in the area. Their goal is to create a unified force to address common threats that endanger peace and stability within the region. Defense diplomacy encompasses a strategic engagement, Confidence Building Measures (CBMs), Capacity Building, and the cultivation of international reputation. The fundamental question arises: How can the EAC engage in security cooperation amid the multitude of issues faced within the region? Our article aims to comprehend the implementation of CBMs among member states, focusing on enhancing interoperability within a case study of regional security cooperation aimed at implementing peace and stability in the Democratic Republic of Congo (DRC). Our research employs qualitative analysis using the concept of Confidence and Security Building Measures (CSBM) and applies DIME theory to illustrate the process of establishing and strengthening trust among member states in the region. The primary outcome of this study underscores the critical role played by diplomacy and military strategies in resolving peace and security issues in the DRC

A systematic methodology to assess the identity of plants in historical texts: A case study based on the Byzantine pharmacy text John the Physician's Therapeutics

Ethnopharmacological relevanceIn recent decades, the study of historical texts has attracted research interest, particularly in ethnopharmacology. All studies of the materia medica cited in ancient and medieval texts share a concern, however, as to the reliability of modern identifications of these substances. Previous studies of European or Mediterranean texts relied mostly on authoritative dictionaries or glossaries providing botanical identities for the historical plant names in question. Several identities they suggest, however, are questionable and real possibility of error exists. Aim of the studyThis study aims to develop and document a novel and interdisciplinary methodology providing more objective assessment of the identity of the plants (and minerals) described in these resources. Materials and methodsWe developed an iterative experimental approach, using the 13th century Byzantine recipe text John the Physician's Therapeutics in its Commentary version (JC) as a case study. The methodology has six stages and relies on comparative analyses including statistical evaluation of botanical descriptions and information about medicinal uses drawn from both historical and modern sources. Stages 1–4 create the dataset, stage 5 derives the primary outcomes to be reviewed by experts in stage 6. ResultsUsing Disocorides’ De Materia Medica (DMM) (1st century CE) as the culturally related reference text for the botanical descriptions of the plants cited in JC, allowed us to link the 194 plants used medicinally in JC with 252 plants cited in DMM. Our test sample for subsequent analyses consisted of the 50 JC plant names (corresponding to 61 DMM plants) for which DMM holds rich morphological information, and the 130 candidate species which have been suggested in the literature as potential botanical identities of those 50 JC plant names. Statistical evaluation of the comparative analyses revealed that in the majority of the cases, our method detected the candidate species having a higher likelihood of being the correct attribution from among the pool of suggested candidates. Final assessment and revision provided a list of the challenges associated with applying our methodology more widely and recommendations on how to address these issues. ConclusionsWe offer this multidisciplinary approach to more evidence-based assessment of the identity of plants in historical texts providing a measure of confidence for each suggested identity. Despite the experimental nature of our methodology and its limitations, its application allowed us to draw conclusions about the validity of suggested candidate plants as well as to distinguish between alternative candidates of the same historical plant name. Fully documenting the methodology facilitates its application to historical texts of any kind of cultural or linguistic background.

Geothermal Play Fairway Analysis, Part 2: GIS methodology

Play Fairway Analysis (PFA) in geothermal exploration originates from a systematic methodology developed within the petroleum industry and is based on a geologic, geophysical, and hydrologic framework of identified geothermal systems. We tailored this methodology to study the geothermal resource potential of the Snake River Plain and surrounding region, but it can be adapted to other geothermal resource settings. We adapted the PFA approach to geothermal resource exploration by cataloging the critical elements controlling exploitable hydrothermal systems, establishing risk matrices that evaluate these elements in terms of both probability of success and level of knowledge, and building a code-based ‘processing model’ to process results.A geographic information system was used to compile a range of different data types, which we refer to as elements (e.g., faults, vents, heat flow, etc.), with distinct characteristics and measures of confidence. Discontinuous discrete data (points, lines, or polygons) for each element were transformed into continuous interpretive 2D grid surfaces called evidence layers. Because different data types have varying uncertainties, most evidence layers have an accompanying confidence layer which reflects spatial variations in these uncertainties. Risk layers, as defined here, are the product of evidence and confidence layers, and are the building blocks used to construct Common Risk Segment (CRS) maps for heat, permeability, and seal, using a weighted sum for permeability and heat, but a different approach with seal. CRS maps quantify the variable risk associated with each of these critical components. In a final step, the three CRS maps were combined into a Composite Common Risk Segment (CCRS) map, using a modified weighted sum, for results that reveal favorable areas for geothermal exploration. Additional maps are also presented that do not mix contributions from evidence and confidence (to allow an isolated view of evidence and confidence), as well as maps that calculate favorability using the product of components instead of a weighted sum (to highlight where all components are present). Our approach helped to identify areas of high geothermal favorability in the western and central Snake River Plain during the first phase of study and helped identify more precise local drilling targets during the second phase of work. By identifying favorable areas, this methodology can help to reduce uncertainty in geothermal energy exploration and development.

Using multimodal, low‐cost data to efficiently identify clinical subgroups and classify Alzheimer’s diagnosis

AbstractBackgroundWith increasingly greater prevalence of Alzheimer’s disease (AD) worldwide, cost‐ and time‐efficient methods for identifying at‐risk individuals are needed. Survey, simple neuropsychological, and demographic information are low‐cost, but their lower levels of sensitivity or specificity when considered individually are problematic. However, past work has demonstrated the usefulness of statistical machine learning tools for predicting clinical diagnosis using multimodal data. To better understand and quantify Alzheimer’s risk and progression at follow‐up using lower‐cost data, we apply statistical and machine learning tools to screen, cluster, and classify individuals from the National Alzheimer’s Coordinating Center (NACC) dataset.MethodPreviously, we developed hierarchical classification strategies to use lower‐cost data for 3‐class prediction (unimpaired, AD‐MCI, and AD‐dementia). Building upon this idea, here we leverage a Gaussian mixture model to infer subgroup identities of subjects and use subgroup identity to further aid classification. Individuals belonging to homogeneous subgroups consisting primarily of a single diagnosis are more likely to also be given that diagnosis. This approach enables confidence in screening given a threshold for identifying the primary diagnosis within each cluster. Additional classification is performed on subjects that belong to heterogeneous subgroups. The NACC database is funded by NIA/NIH Grant U24 AG072122. NACC data are contributed by the NIA‐funded ADRCs.ResultThe proposed method unmasks the clinical subgroup trends within the sample and allows for detailed longitudinal risk tracking. We identified five subgroups within the sample, representing five clinical clusters. These subgroups include predominantly unimpaired individuals, mixtures of unimpaired and MCI, MCI and AD‐dementia, and individuals predominantly with AD‐dementia. A large proportion of individuals can be confidently screened based upon subgroup identity. Using only baseline visit information, predictions of clinical diagnosis post‐clustering is highly reflective of conversion to a worse diagnosis within 18 months. Changes in the conversion rate to a more severe diagnosis are dependent on the cluster identity.ConclusionUsing cost‐ and time‐efficient data enables more effective screening and tracking of disease progression. The proposed approach of clustering, screening, and prediction yields a measure of confidence in predictions of clinical diagnosis and allows for greater understanding of clinical profiles and progression.

Deep learning structural insights into heterotrimeric alternatively spliced P2X7 receptors.

P2X7 receptors (P2X7Rs) are membrane-bound ATP-gated ion channels that are composed of three subunits. Different subunit structures may be expressed due to alternative splicing of the P2RX7 gene, altering the receptor's function when combined with the wild-type P2X7A subunits. In this study, the application of the deep-learning method, AlphaFold2-Multimer (AF2M), for the generation of trimeric P2X7Rs was validated by comparing an AF2M-generated rat wild-type P2X7A receptor with a structure determined by cryogenic electron microscopy (cryo-EM) (Protein Data Bank Identification: 6U9V). The results suggested AF2M could firstly, accurately predict the structures of P2X7Rs and secondly, accurately identify the highest quality model through the ranking system. Subsequently, AF2M was used to generate models of heterotrimeric alternatively spliced P2X7Rs consisting of one or two wild-type P2X7A subunits in combination with one or two P2X7B, P2X7E, P2X7J, and P2X7L splice variant subunits. The top-ranking models were deemed valid based on AF2M's confidence measures, stability in molecular dynamics simulations, and consistent flexibility of the conserved regions between the models. The structure of the heterotrimeric receptors, which were missing key residues in the ATP binding sites and carboxyl terminal domains (CTDs) compared to the wild-type receptor, help to explain their observed functions. Overall, the models produced in this study (available as supplementary material) unlock the possibility of structure-based studies into the heterotrimeric P2X7Rs.

Autonomic nervous system activity in response to stress and cardiac coherence breathing exercise depending on body composition differences

ABSTRACT This study investigated the effects of stress and cardiac coherence breathing exercises on psychological and psychophysiological responses, and also determined whether these effects differed depending on body composition. The study participants included 33 individuals with a favourable body composition in the fit group and 25 individuals with a less favourable body composition in the unfit group. Before the experiment, the participants completed psychological questionnaires to determine psychological differences at baseline. We recorded an electrocardiogram to analyze heart rate variability (HRV) for five minutes under three conditions: rest, an arithmetic task to induce stress, and a breathing exercise. This was followed by measurements of stress, anxiety, relaxation, and confidence using the visual analogue scale. Baseline analyses revealed that the fit group had lower depression, state and trait anxiety, and stress levels than the unfit group. The HRV indices of the standard deviation of the N-N interval (SDNN) and the root mean square of successive difference (RMSSD) decreased during the arithmetic task with increased stress and heart rate. In contrast, the breathing exercise increased SDNN, RMSSD, total power, and low-frequency power, reduced anxiety and stress, and increased relaxation and confidence. The stress condition and breathing exercise affected the groups differently. The fit group demonstrated a more significant increase in sympathetic activity during stress situations, followed by a greater increase in parasympathetic activity and recovery from stress during the breathing exercise. These findings illustrate that individuals with a more favourable body composition may have greater autonomic nervous system adaptability.

Bias Cuts and Data Dumps

Bias Cuts and Data Dumps

Evaluation of a Coach-Centered Youth ACL Injury Risk Reduction Workshop Using RE-AIM Sports Setting Framework

ABSTRACT Introduction/Purpose Our aim was to evaluate the feasibility and short-term effectiveness of a community-based anterior cruciate ligament (ACL) injury risk reduction program (IRP) live workshop using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework modified for sports settings. Methods We conducted a prospective longitudinal study. Custom surveys were administered before and after the workshop and 1 month later to capture contextual variables needed to evaluate the program using four of the five RE-AIM domains. Live workshops were conducted at community centers throughout the northeastern US, and coaches were trained on risk factors for ACL injuries and how to identify and correct musculoskeletal movements known to increase ACL injury risk. Results Three hundred and seventy-three coaches attended the workshop, but 171 (46.4%; mean age, 33 ± 10 yr; 58% male) completed pre- and post-workshop surveys demonstrating significant improvements (P &lt; 0.0001) in measures of program effectiveness, including knowledge (8.1%), attitudes/beliefs (18.0%), and confidence (39.6%). Overall, 98% of coaches were satisfied with the workshop delivery quality and content. Fifty-three coaches completed a 1-month follow-up survey, 75.5% of whom reported adoption of the ACL IRP. Of these, 100% reported implementing the ACL IRP before practice/training sessions, and 97.5% reported implementing it before games/competitions. Conclusions Overall, the results of this evaluation demonstrate the ACL IRP workshop feasibility and provide guidance for enhancement of the workshop. Our ACL IRP workshop was effective at increasing coaches’ knowledge and perspectives about injury risk reduction principles. Although the behavioral strategies incorporated in the program design promoted high ACL IRP workshop satisfaction, further improvements are needed to increase survey response rates during post-workshop follow-up.

A survey of Indonesian nurses' educational experiences and self-perceived capability to care for people with intellectual disability and/or autism spectrum disorder.

To describe Indonesian nurses' educational experience regarding care for people with intellectual disability and/or autism and to explore if these educational experiences are associated with their self-perceived confidence, comfort, knowledge and preparedness to care for these cohorts. Cross-sectional descriptive survey. A descriptive survey tool utilized in a study of Australian registered nurses was replicated and adapted for this study. Following descriptive analysis, chi-square analyses were undertaken to explore associations between educational experiences, and self-rated measures of confidence, comfort and knowledge to work with people with intellectual disability and/or autism. Although there was no end-user involvement in the design of the study, the concepts explored have all been raised by those with lived insights of intellectual disability and/or autism as being critical to their healthcare experiences. There were 544 complete responses, and 51.7% were not exposed to any educational or clinical content relevant to caring for people with intellectual disability and/or autism. Moderate to low levels of self-perceived confidence, comfort, knowledge and preparedness to address healthcare needs of these cohorts were reported. Significant associations were identified between educational and clinical experiences during undergraduate training, and higher levels of self-perceived confidence, comfort and knowledge. Mirroring international literature, the findings of this study highlight a large proportion of Indonesian nurses had little educational experience relevant to caring for people with intellectual disability and/or autism, and have relatively low levels of self-reported capability. This study highlights gaps in the educational experiences, and self-perceived confidence, comfort, knowledge and preparedness of Indonesian nurses regarding caring for people with intellectual disability and/or autism. Given that internationally, people with intellectual disability and/or autism have disproportionately negative health outcomes and experiences, these findings have substantial implications for nursing curriculum, policy and professional development.

Evaluation of Summer Non‐Uniform Multidecadal Temperature Variations Over Eurasia in CMIP6 Models

AbstractThe summer surface air temperatures (SAT) over Eurasia exhibit a non‐uniform multidecadal pattern since the 1990s, with amplified warming over Europe–West Asia (EWA) and Northeast Asia (NEA) but weak warming over Central Asia. However, current climate models may inadequately capture this regional pattern. In this study, we evaluated the performance of 44 coupled models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating the decadal variability of Eurasian temperatures. Results show that all the CMIP6 models generally reproduce the largest SAT trends from 1985 to 2020 over EWA and NEA. However, the models fail to capture the non‐uniform warming pattern and the SAT magnitudes are underestimated in most CMIP6 models. We compare “good” and “poor” ensemble, which represent the best and worst simulations of the non‐uniform warming, in order to understand the inter‐model spread. The BMME models capture the teleconnection wave trains from northwest Europe to East Asia, and the associated summer North Atlantic Oscillation like circulation changes. In contrast, the WMME models fail to reproduce such teleconnections. The contrast between the BMME and WMME simulations give a measure of confidence that the non‐uniform warming pattern over Eurasia is tightly coupled with the upper‐tropospheric wave‐like pattern over Eurasia. If such atmospheric circulation changes cannot be reproduced, the non‐uniform warming pattern over Eurasia will not be captured. Our results suggest a fundamental pattern of atmospheric circulation changes that determine the non‐uniform warming variations in model simulations.

Abstract 307: In-Situ Simulation-Based Booster ACLS Training to Increase the Confidence and Performance of Internal Medicine Residents as Code Blue Leaders - A Quality Improvement Project

Background: In the United States, nearly 290,000 adult patients develop in-hospital cardiac arrest (IHCA) every year. Delivery of guideline-based resuscitation has improved patient outcomes. During 2022, our hospital had 68 inpatient i.e., approximately 1 code/resident. Residents have reported desire for more exposure. The 2020 AHA Guidelines for Cardiopulmonary Resuscitation and Emergency Cardiovascular Care recommended: 1) booster training, 2) in-situ simulation-based resuscitation training, and 3) specific teamwork and leadership training for healthcare providers. We aimed to implement such training to achieve an improvement in resident confidence and performance as code blue leaders. Methods: Pre-intervention, residents completed a 5-item 0-100 continuous scale to assess confidence in major code leader skills (Fig 1-A). Interventions included: 1) Calling residents to patient rooms to run simulation-based codes, and 2) a didactic session that discussed common errors during codes. Time to key ACLS steps was recorded during the simulations. At the end of the project, residents completed the post-intervention survey of confidence and balancing measures. Results: 13 residents led simulation-based codes, 20 attended the didactic session, and 25 completed the survey. Resident confidence in the defined skills increased by an average of 30%. (P&lt;0.05) (Fig 1-A). Residents who led codes after the didactic session had shorter times to key ACLS steps (Fig 1-B). Balancing measures included resident-reported satisfaction and perceived disruption to clinical workflow (100% and 0% of responses, respectively). Conclusions: In-situ simulation-based booster ACLS training was associated with an increase in the confidence and performance of IM residents as code blue leaders. Incorporation of such training in IM residency curriculum should be considered as a practical application to sustain the gains of this QI initiative.

Interactive Web-based Emergency Neuroradiology Course with Self-Assessment for Radiology Residents: A Pilot Project.

To assess the effectiveness of an online, case-based interactive course in emergency neuroradiology to prepare radiology residents for night call in neuroradiology. A total of 15 residents participated in a pretest assessment of preparedness for neuroradiology call. After completing a 6-week interactive course incorporating case review, didactic lectures, quiz feedback, and references for further review, the same residents were quizzed on cognitive questions and feelings of readiness to enter the on-call pool for neuroradiology. Knowledge and confidence both significantly increased due to the course. Knowledge-wise, the scores for precourse quiz to postcourse quiz went from 18.4%-72.2% (53.8% increase) in Brain Imaging and went from 22.3%-77.1% (54.8% increase) in Head, Neck, and Spine (P < 0.001 for both). Confidence-wise, residents demonstrated statistically significant increases in all 6 confidence measures. Prior to the course, 29% were not confident, 71% were fairly confident, and 0% were confident/very confident. After the course, 0% were not confident, 43% were fairly confident, and 57% were confident/very confident. Belief in the statement "I can provide high quality Neuroimaging services in the emergency care setting" increased from a confidence score of 1.29-2.57 after training (P=0.004). Nearly all residents completing their first emergency call reported that they felt more confident reading neuroradiology studies during their call as a direct result of the course. Completing the multi-pronged interactive, case-based online emergency neuroradiology course led to improved funds of knowledge and feelings of confidence and impacted imaging approach in residents taking neuroradiology call.

Representing model uncertainties in brittle fracture simulations

This work focuses on the representation of model-form uncertainties in phase-field models of brittle fracture. Such uncertainties can arise from the choice of the degradation function for instance, and their consideration has been unaddressed to date. The stochastic modeling framework leverages recent developments related to the analysis of nonlinear dynamical systems and relies on the construction of a stochastic reduced-order model. In the latter, a POD-based reduced-order basis is randomized using Riemannian projection and retraction operators, as well as an information-theoretic formulation enabling proper concentration in the convex hull defined by a set of model proposals. The model thus obtained is mathematically admissible in the almost sure sense and involves a low-dimensional hyperparameter, the calibration of which is facilitated through the formulation of a quadratic programming problem. The relevance of the modeling approach is further assessed on one- and two-dimensional applications. It is shown that model uncertainties can be efficiently captured and propagated to macroscopic quantities of interest. An extension based on localized randomization is also proposed to handle the case where the forward simulation is highly sensitive to sample localization. This work constitutes a methodological development allowing phase-field predictions to be endowed with statistical measures of confidence, accounting for the variability induced by modeling choices.