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Optimizing structural designs is crucial today, with additive manufacturing, particularly selective laser melting, gaining prominence. Thorough mechanical characterization of new materials remains vital. This paper investigates fatigue crack growth behavior in SLM 316L specimens under cyclic loading conditions. The study addresses result uncertainties by using CT specimens aligned along three building directions per ASTM E647 standards and a constant loading ratio (R = 0.1), necessitating mean value and confidence interval predictions. Departing from linear prediction models, innovative Bootstrap Polynomial and Power Regression Models and Bayesian Nonlinear Regression Model updated posterior distribution by Markov Chain Monte Carlo are employed. These approaches leverage bootstrapping to construct confidence intervals, offering robustness and flexibility in handling non‐normal data behavior and limited sample sizes. They provide tailored fits to data curvature, revealing limitations of linear prediction models in capturing observed nonlinear behavior, enhancing reliability in additive manufacturing applications, and advancing material science and engineering. [ABSTRACT FROM AUTHOR]

Background: Previous studies on whole grain consumption had inconsistent findings and lacked quantitative assessments of evidence quality. Therefore, we aimed to summarize updated findings using the Burden of Proof analysis (BPRF) to investigate the relationship of whole grain consumption on type 2 diabetes (T2D), colorectal cancer (CRC), stroke, and ischemic heart disease (IHD). Methods: We conducted a literature search in the Medline and Web of Science up to June 12, 2023, to identify related cohort studies and systematic reviews. The mean RR (relative risk) curve and uncertainty intervals (UIs), BPRF function, risk-outcome score (ROS), and the theoretical minimum risk exposure level (TMREL) were estimated to evaluate the level of four risk-outcome pairs. Results: In total, 27 prospective cohorts were included in our analysis. Consuming whole grain at the range of TMREL (118.5–148.1 g per day) was associated with lower risks: T2D (declined by 37.3%, 95% UI: 5.8 to 59.5), CRC (declined by 17.3%, 6.5 to 27.7), stroke (declined by 21.8%, 7.3 to 35.1), and IHD (declined by 36.9%, 7.1 to 58.0). For all outcomes except stroke, we observed a non-linear, monotonic decrease as whole grain consumption increased; For stroke, it followed a J-shaped curve (the greatest decline in the risk of stroke at consuming 100 g whole grain for a day). The relationships between whole grain consumption and four diseases are all two-star pairs (ROS: 0.087, 0.068, 0.062, 0.095 for T2D, CRC, stroke, and IHD, respectively). Conclusion: Consuming 100 g of whole grains per day offers broad protective benefits. However, exceeding this threshold may diminish the protective effects against stroke. Our findings endorse replacing refined grains with whole grains as the main source of daily carbohydrates. Registry and registry number for systematic reviews or meta-analyses: We have registered our research in PROSPERO, and the identifier of our meta-analyses is CRD42023447345. [ABSTRACT FROM AUTHOR]

Ocean wave spectrum is the key to the response estimation of seagoing vessel whose structural integrity is of utmost importance. Efforts have been made by researchers to correctly estimate the ocean wave spectrum using so called 'wave-buoy analogy' concept, where the vessel is considered to behave as a wave buoy. The aim of this study is to develop a methodology through which the directional wave spectrum can be estimated using the concept of 'wave-buoy analogy'. To achieve the objective, ocean wave was modeled with 10-parameter bimodal wave spectrum combining long- and short-wave component. These 10 parameters of bimodal wave spectrum were targeted by solving non-linear least square problem, which is formulated by error function quantifying the difference between model prediction and onboard measurement data. Model prediction is based on the linear relationship between the wave spectrum and response spectra and measurement data are directly from the sensors installed on the vessel. To solve the non-linear least square problem, Bayesian statistics-based probabilistic approaches, Markov-Chain Monte Carlo simulation (MCMC), were utilized. Well-known adaptive Metropolis–Hastings algorithm which is one of the most popularly used MCMC techniques was utilized to derive the spectrum parameters that best describe the directional wave spectrum. To validate the proposed methodology, pseudo measurement data generated by numerical analysis with different loading conditions were used. The application of the proposed methodology to the numerical analysis data confirmed that it accurately estimates the response at locations where sensors are not installed. [ABSTRACT FROM AUTHOR]

Clinical trials play a crucial role in generating evidence about healthcare interventions and improving outcomes for current and future patients. For individual trial participants, however, there are inevitably trade‐offs involved in clinical trial participation, given that trials have traditionally been designed to benefit future patient populations rather than to offer personalised care. Failure to understand the distinction between research and clinical care and the likelihood of benefit from participation in clinical trials has been termed the 'therapeutic misconception'. The evolution of the clinical trials landscape, including greater integration of clinical trials into healthcare and development of novel trial methodologies, may reinforce the significance of the therapeutic misconception and other forms of misunderstanding while at the same time (paradoxically) challenging its salience. Using cancer clinical trials as an exemplar, we describe how methodological changes in early‐ and late‐phase clinical trial designs, as well as changes in the design and delivery of healthcare, impact upon the therapeutic misconception. We suggest that this provides an impetus to re‐examine the ethics of clinical research, particularly in relation to trial access, participant selection, communication and consent, and role delineation. [ABSTRACT FROM AUTHOR]

In many applications of data assimilation, especially when the size of the problem is large, a substantial assumption is made: all variables are well‐described by Gaussian error statistics. This assumption has the advantage of making calculations considerably simpler, but it is often not valid, leading to biases in forecasts or, even worse, unphysical predictions. We propose a simple, but effective, way of replacing this assumption, by making use of transforming functions, while remaining consistent with Bayes' theorem. This method allows the errors to have any value of the skewness and kurtosis, and permits physical bounds for the variables. As such, the error distribution can conform better to the underlying statistics, reducing biases introduced by the Gaussian assumption. We apply this framework to a 3D variational data assimilation method, and find improved performance in a simple atmospheric toy model (Lorenz‐63), compared to an all‐Gaussian technique. Plain Language Summary: Data assimilation is the technique of combining model predictions with observations in the optimal way and is, for example, used in weather forecasting. To simplify the procedure, a crucial assumption is made on the statistics of all components. However, as models are becoming increasingly precise, and computational techniques are improving, it is becoming more and more obvious that this simple assumption is inadequate, resulting in inaccurate forecasts. We provide a very general framework that relaxes this assumption, and that allows for a much broader scope of statistics. This generalization significantly improves prevailing methods, as tested with a simple atmospheric toy model. Key Points: A general framework for non‐Gaussian data assimilation is formulatedThe proposed method allows for skewness and kurtosis and provides a straightforward approach to handling variables with physical boundsExperiments with the Lorenz‐63 model show significant improvements over an all‐Gaussian approach [ABSTRACT FROM AUTHOR]

Monitoring life-testing trials for a product or substance often demands significant time and effort. To expedite this process, sometimes units are subjected to more severe conditions in what is known as accelerated life tests. This paper is dedicated to addressing the challenge of estimating the power hazard distribution, both in terms of point and interval estimations, during constant- stress partially accelerated life tests using progressive first failure censored samples. Three techniques are employed for this purpose: maximum likelihood, two parametric bootstraps, and Bayesian methods. These techniques yield point estimates for unknown parameters and the acceleration factor. Additionally, we construct approximate confidence intervals and highest posterior density credible intervals for both the parameters and acceleration factor. The former relies on the asymptotic distribution of maximum likelihood estimators, while the latter employs the Markov chain Monte Carlo technique and focuses on the squared error loss function. To assess the effectiveness of these estimation methods and compare the performance of their respective confidence intervals, a simulation study is conducted. Finally, we validate these inference techniques using real-life engineering data. [ABSTRACT FROM AUTHOR]

In this study, we introduce a novel estimation technique for assessing the reliability parameter R = P (Y < X) of the uniform truncated negative binomial distribution (UTNBD) in the context of stress–strength analysis. We base our inferences on the assumption that both the strength (X) and stress (Y) random variables follow a UTNBD with identical first shape and scale parameters. In the presence of a progressive type-II censoring scheme, we employ maximum likelihood, two parametric bootstrap methods, and Bayesian estimation approaches to derive the estimators. Due to the complexity introduced by censoring, the estimators are not available in explicit forms and are instead obtained through numerical approximation techniques. Furthermore, we compute the highest posterior density credible intervals and determine the asymptotic variance-covariance matrix. To assess the performance of our proposed estimators, we conduct a Monte Carlo simulation study and provide a comparative analysis. Finally, we illustrate the practical applicability of our study with an engineering application. [ABSTRACT FROM AUTHOR]

Background: The incidence rate of stroke or cerebrovascular accidents ranks first in China. More than 85% of stroke patients have residual upper limb motor dysfunction, especially hand dysfunction. Normalizing the rehabilitation evaluation process and standard quantitative evaluation method is a complex and key point in rehabilitation therapy. The study aimed to establish a function model based on the Bayes discriminant by measuring the thenar stiffness with shear wave elastography (SWE) to quantitatively evaluate the hand motor function of hemiplegic patients after stroke. Methods: This study collected 60 patients diagnosed with hemiplegia after stroke from October 2021 to October 2022. Therapists used the Brunnstrom assessment (BA)scale to divide the patients into the stage. All the patients underwent the measurement of SWE examination of abductor pollicis brevis (APB), opponens pollicis (OP), flexor pollicis long tendon (FPLT), and flexor pollicis brevis (FPB) by two sonographers. The SWE change rate of four parts of the thenar area was calculated prospectively with the non-hemiplegic side as the reference, the function equation was established by the Bayes discriminant method, and the evaluation model was fitted according to the acquired training set data. Lastly, the model was verified by self-validation, cross-validation, and external data validation methods. The classification performance was evaluated regarding the area under the ROC curve (AUC), sensitivity, and specificity. Results: The median SWE values of the hemiplegic side of patients were lower than those of the non-hemiplegic side. According to the BA stage and SWER of APB, OP, FPLT, and FPB, our study established the Bayes discriminative model and validated it via self-validation and cross-validation methods. Then, the discriminant equation was used to validate 18 patients prospectively, the diagnostic coincidence rate was about 78.8%, and the misjudgment rate was approximately 21.2%. The AUC of the discriminant model for diagnosing BA stage I-VI was 0.928(95% CI: 0.839-1.0),0.858(95% CI: 0.748–0.969),1.0(95% CI: 1.0–1.0), 0.777(95% CI: 0.599–0.954),0.785(95% CI: 0.593–0.977) and 0.985(95% CI: 0.959-1.0), respectively. Conclusion: This Bayes discriminant model built by measuring thenar stiffness was of diagnostic value and can provide an objective basis for evaluating clinical rehabilitation. [ABSTRACT FROM AUTHOR]

Acute severe cardiac valve emergencies, such as acute severe mitral regurgitation (AMR) and acute severe aortic regurgitation (AAR), present significant challenges in terms of diagnosis and management. Handheld point-of-care ultrasound devices have emerged as potentially pivotal tools in ensuring the prompt and accurate diagnosis of these left-sided valve emergencies by emergency physicians, particularly in resource-limited settings. Despite the increased utilisation of point-of-care ultrasound by emergency physicians for the management of patients in states of acute cardiorespiratory failure, current diagnostic protocols cannot perform sufficient quantitative assessments of the left-sided cardiac valves. This review elucidates and evaluates the diagnostic utility of handheld point-of-care focused-echocardiography (HoPE) in native AMR and AAR by reviewing the relevant literature and the use of clinical case examples from the Emergency Department at Port Shepstone Regional Hospital (PSRH-ED)—a rural, resource-limited hospital located in KwaZulu-Natal, South Africa. Combining the findings of the review and clinical case illustrations, this review proceeds to synthesise a novel, Bayesian-inspired, iterative diagnostic framework that integrates HoPE into the evaluation of patients with acute cardiorespiratory failure and suspected severe left-sided valve lesions. [ABSTRACT FROM AUTHOR]

Diagnostic error is not uncommon and diagnostic accuracy can be improved with the use of problem representation, pre-test probability, and Bayesian analysis for improved clinical reasoning. A 48-year-old female presented as a transfer from another Emergency Department (ED) to our ED with crushing, substernal pain associated with dyspnea, diaphoresis, nausea, and a tingling sensation down both arms with radiation to the back and neck. Troponins were elevated along with an abnormal electrocardiogram. A negative myocardial perfusion scan led to the patient's discharge. The patient presented to the ED 10 days later with an anterior ST-elevation myocardial infarction. An overemphasis on a single testing modality led to diagnostic error and a severe event. The use of pre-test probabilities guided by history-taking can lead to improved interpretation of test results, ultimately improving diagnostic accuracy and preventing serious medical errors. [ABSTRACT FROM AUTHOR]

Background: Sporadic lymphangioleiomyomatosis (S-LAM) is a rare low-grade neoplasm of young women characterized by multiple pulmonary cysts leading to progressive dyspnea and recurrent spontaneous pneumothorax (SP). The diagnosis of S-LAM may be delayed by several years. To reduce this delay, chest computed tomography (CT) screening has been proposed to uncover cystic lung disease in women presenting with SP. However, the probability to discover S-LAM in this population has not been determined precisely. The aim of this study was to calculate the probability of finding S-LAM in women presenting with (a) SP, and (b) apparent primary SP (PSP) as first manifestation of S-LAM. Methods: Calculations were made by applying the Bayes theorem to published epidemiological data on S-LAM, SP and PSP. Each term of the Bayes equation was determined by meta-analysis, and included: (1) the prevalence of S-LAM in the general female population, (2) the incidence rate of SP and PSP in the general female population, and (3) the incidence rate of SP and apparent PSP in women with S-LAM. Results: The prevalence of S-LAM in the general female population was 3.03 per million (95% confidence interval 2.48, 3.62). The incidence rate of SP in the general female population was 9.54 (8.15, 11.17) per 100,000 person-years (p-y). The incidence rate of SP in women with S-LAM was 0.13 (0.08, 0.20). By combining these data in the Bayes theorem, the probability of finding S-LAM in women presenting with SP was 0.0036 (0.0025, 0.0051). For PSP, the incidence rate in the general female population was 2.70 (1.95, 3.74) per 100,000 p-y. The incidence rate of apparent PSP in women with S-LAM was 0.041 (0.030, 0.055). With the Bayes theorem, the probability of finding S-LAM in women presenting with apparent PSP as first disease manifestation was 0.0030 (0.0020, 0.0046). The number of CT scans to perform in women to find one case of S-LAM was 279 for SP and 331 for PSP. Conclusion: The probability of discovering S-LAM at chest CT in women presenting with apparent PSP as first disease manifestation was low (0.3%). Recommending chest CT screening in this population should be reconsidered. [ABSTRACT FROM AUTHOR]

Studying the ages of mobile phones is considered one of the most important things in the recent period in the field of shopping and modern technology. In this paper, we will consider that the ages of these phones follow a gamma distribution under progressive first-failure (PFF) censoring. All of the unknown parameters, as well as Shannon and Rényi entropies, were estimated for this distribution. The maximum likelihood (ML) approach was utilized to generate point estimates for the target parameters based on the considered censoring strategy. The asymptotic confidence intervals of the ML estimators (MLEs) of the targeted parameters were produced using the normal approximation to ML and log-transformed ML. We employed the delta method to approximate the variances of the Shannon and Rényi functions to obtain their asymptotic confidence intervals. Additionally, all parameter estimates utilized in this study were determined using the successful expectation–maximization (EM) method. The Metropolis–Hastings (MH) algorithm was applied to construct the Bayes estimators and related highest posterior density (HPD) credible intervals under various loss functions. Further, the proposed methodologies were contrasted using Monte Carlo simulations. Finally, the radio transceiver dataset was analyzed to substantiate our results. [ABSTRACT FROM AUTHOR]

Patients with critical neurological illness are diverse. As a result of the heterogeneity of this patient population, standardized approaches to patient management might not confer benefit. A precision medicine approach to neurocritical care is therefore urgently needed to improve our understanding of neurocritical illness and the care provided to this vulnerable cohort. Research designs and approaches based on Bayesian models have the potential to meet this need, as they are specifically designed to evolve with emerging evidence. This adaptability provides a benefit over the popular frequentist statistical approach, as it provides a way of adjusting hypotheses and trial procedures to maximize efficacy. This review summarizes the current state of knowledge on Bayes' theorem, and its potential applications to the field of neurocritical care. We review the basic principles underlying Bayes' theorem, compare the use of Bayesian versus frequentist statistics in medicine, and discuss the relevance of Bayesian statistics to the field of neuroscience and to clinical research. Finally, we explore the potential benefits of employing Bayesian methods within the field of neurocritical care as a steppingstone toward implementing precision medicine approaches to improve patient outcomes for complex, heterogeneous disorders. [ABSTRACT FROM AUTHOR]

Background: Trends in the prevalence of hepatitis C virus (HCV) infection among women delivering live births may differ in rural vs. urban areas of the United States, but estimation of trends based on observed counts may lead to unstable estimates in rural counties due to small numbers. Objectives: The objective of the study was to use small area estimation methods to provide updated county‐level prevalence estimates and, for the first time, trends in maternal HCV infection among live births by county‐level rurality. Methods: Cross‐sectional natality data from 2016 to 2020 were used to estimate maternal hepatitis C prevalence using hierarchical Bayesian models with spatiotemporal random effects to produce annual county‐level estimates of maternal HCV infection and trends over time. Models included a 6‐level rural–urban county classification, year, maternal characteristics and county‐specific covariates. Data were analysed in 2022. Results: There were 90,764/18,905,314 live births (4.8 per 1000) with HCV infection reported on the birth certificate. Hepatitis C prevalence was higher among rural counties as compared to urban counties. Rural counties had the largest annual increases in maternal hepatitis C prevalence (per 1000 births) from 2016 to 2020 (micropolitan: 0.39; noncore: 0.40), with smaller increases among less densely populated urban counties (medium metro: 0.28; small metro: 0.28) and urban counties (large central metro:0.11; large fringe metro: 0.14). Conclusions: The prevalence of maternal HCV infection was the highest in rural counties, and rural counties saw the greatest average prevalence increase during 2016–2020. County‐level data can help in monitoring rural–urban trends in maternal HCV infection to reduce geographic disparities. [ABSTRACT FROM AUTHOR]

The aim of this research is to create a medical expert system based on Bayes theorem to diagnose level of disc hernia based on real foot force measurement signals obtained using sensors and implement the whole system on field programable gate array (FPGA). We have created a database of attributes based on recorded foot force values of 33 patients pre-diagnosed with herniated disc on levels L4/L5 or L5/S1 on the left or right side. The results obtained by software (Matlab) and hardware (FPGA simulation) are matching well, achieving high accuracy, which shows that VHDL implementation of Naïve Bayes theorem for disc hernia diagnostics is adequate. The output on FPGA is easy to understand for any user, as it is implemented as four-bit output where the position of bit value 1 indicates the level of disc herniation. The system is able to distinguish between the healthy subjects and subjects with disc herniation and is able to detect if improvement in stability is present after surgery or physical therapy. Our proposed measurement platform can be coupled with FPGA to create a portable and not expensive tool for real time signal acquisition, processing and decision support system in disc hernia diagnosis and post-surgical recovery. [ABSTRACT FROM AUTHOR]

Objective: While declarative learning is dependent on the hippocampus, procedural learning and repetition priming can operate independently from the hippocampus, making them potential targets for behavioral interventions that utilize non-declarative memory systems to compensate for the declarative learning deficits associated with hippocampal insult. Few studies have assessed procedural learning and repetition priming in individuals with amnestic mild cognitive impairment (aMCI). Method: This study offers an overview across declarative, conceptual repetition priming, and procedural learning tasks by providing between-group effect sizes and Bayes Factors (BFs) comparing individuals with aMCI and controls. Seventy-six individuals with aMCI and 83 cognitively unimpaired controls were assessed. We hypothesized to see the largest differences between individuals with aMCI and controls on declarative learning, followed by conceptual repetition priming, with the smallest differences on procedural learning. Results: Consistent with our hypotheses, we found large differences between groups with supporting BFs on declarative learning. For conceptual repetition priming, we found a small-to-moderate between-group effect size and a non-conclusive BF somewhat in favor of a difference between groups. We found more variable but overall trivial differences on procedural learning tasks, with inconclusive BFs, in line with expectations. Conclusions: The current results suggest that conceptual repetition priming does not remain intact in individuals with aMCI while procedural learning may remain intact. While additional studies are needed, our results contribute to the evidence-base that suggests that procedural learning may remain spared in aMCI and helps inform behavioral interventions that aim to utilize procedural learning in this population. [ABSTRACT FROM AUTHOR]

Objectives: Determining the clinical homogeneous and heterogeneous sets among depressive patients is the key to facilitate individual-level treatment decision. Methods: The diffusion tensor imaging (DTI) data of 62 patients with major depressive disorder (MDD) and 39 healthy controls were used to construct a Latent Dirichlet Allocation (LDA) Bayesian model. Another 48 MDD patients were used to verify the robustness. The LDA model was employed to identify both shared and unique imaging-derived factors of two typically antidepressant-targeted depressive patients, selective serotonin reuptake inhibitors (SSRIs) and serotonin norepinephrine reuptake inhibitors (SNRIs). Furthermore, we applied canonical correlation analysis (CCA) between each factor loading and Hamilton depression rating scale (HAMD) sub-score, to explore the potential neurophysiological significance of each factor. Results: The results revealed the imaging-derived connectional fingerprint of all patients could be situated along three latent factor dimensions; such results were also verified by the out-of-sample dataset. Factor 1, uniquely expressed by SNRI-targeted patients, was associated with retardation (r = 0.4, p = 0.037) and characterized by coupling patterns between default mode network and cognitive control network. Factor 3, uniquely expressed by SSRI-targeted patients, was associated with cognitive impairment (r = 0.36, p = 0.047) and characterized by coupling patterns within cognitive control and attention network, and the connectivity between threat and reward network. Shared factor 2, characterized by coupling patterns within default mode network, was associated with anxiety (r = 0.54, p = 0.005) and sleep disturbance (r = 0.37, p = 0.032). Conclusions: Our findings suggested that quantification of both homogeneity and heterogeneity within MDD may have the potential to inform rational design of pharmacological therapies. Key Points: • The shared and unique manifestations guiding pharmacotherapy of depressive patients are caused by the homogeneity and heterogeneity of underlying structural connections of the brain. • Both shared and unique factor loadings were found in different antidepressant-targeted patients. • Significant correlations between factor loading and HAMD sub-scores were found. [ABSTRACT FROM AUTHOR]

To verify the accuracy of an online algorithm using Bayes' theorem for diagnosing ectopic pregnancy (EP) using human chorionic gonadotropin (hCG), ultrasound, and clinical data in a real cohort. A retrospective cohort study. Gynecologic emergency unit in a tertiary teaching hospital. First-trimester pregnant women who attended the gynecologic emergency unit for any reason. Those who had <13 weeks of pregnancy confirmed by a recent positive pregnancy test; a digital image or electronic report of transvaginal ultrasound (TVUS) obtained from hospital database; and a follow-up with a pathology report or a clinical resolution of a confirmed pregnancy were included in the study. Clinical signs and symptoms, the presence of risk factors for EP, the TVUS findings in each consultation, and the hCG levels were independent variables obtained from the electronic medical records. From these data, the pretest probability, based on the clinical presentation and risk factors, and the likelihood ratio for each variable were calculated for their use in the algorithm, yielding a posttest probability. Not applicable. The accuracy of the online algorithm to identify cases of EP using clinical signs and symptoms, the presence of risk factors for EP, the TVUS findings in each consultation, and the hCG levels. The main outcome was EP, confirmed either by pathology report or by the presence of fetal heartbeat or gestational sac outside the uterine cavity. Between January 1, 2009 and December 27, 2016, 2,495 women were analyzed, and the algorithm was applied to 2,185 of them. The incidence of EP was 8.5% (212/2,495); 310 women were excluded because they were submitted to surgery with decision thresholds <95%. The algorithm was applied to 2,185 women. Just one case remained inconclusive after 3 consultations, and it was considered as an error in prediction. The sensitivity, specificity, and accuracy values (95% confidence interval) of the algorithm were 98.9% (96.1%–99.8%), 98.9% (98.3%–99.2%), and 98.9% (98.3%–99.2%), respectively. The accuracy of the Bayesian algorithm to confirm or rule out EP is excellent. Online Nomogram https://docs.google.com/spreadsheets/d/1jStXlMBjbPyDf6_W0deKGKQLZHU5EFAe8rLhNVPuJuY/edit?usp=sharing [ABSTRACT FROM AUTHOR]

The most recent consensus guidelines for dosing and monitoring vancomycin recommended the use of area‐under‐the‐curve with Bayesian estimation for therapeutic monitoring. As this is a modern concept in the practice of clinical pharmacy, the main objective of this review is to introduce the fundamentals of Bayesian estimation and its mathematical application as it relates to vancomycin therapeutic drug monitoring. In addition, we aim to identify pharmacokinetic (PK) software programs that incorporate Bayesian estimation for vancomycin dosing and to describe the PK models utilized in those software programs for the adult population. Twelve software programs that utilize Bayesian estimation were identified, which included: Adult and Pediatric Kinetics, Best Dose, ClinCalc, DoseMeRx, ID‐ODS, InsightRx, MwPharm++, NextDose, PrecisePK, TDMx, Tucuxi, and VancoCalc. The software programs varied in the population PK models used as the Bayesian a priori. With the presence of various vancomycin Bayesian software programs, it is important to choose those that utilize PK models reflective of the specific patient population. [ABSTRACT FROM AUTHOR]

The paper demonstrates a successful application of Bayesian classification method to accurately predict petrophysical properties and lithofacies classification in the deep unconventional (tight gas) hydrocarbon resource potential of early Cretaceous in the Lower Indus Basin of Pakistan. To explore the true potential for exploration and development phases, we quantitatively characterized the tight gas reservoir based on an integrated methodology using the Bayesian approach constraint with rock physics analysis which utilized deterministic petrophysical results from a well information to extract the desired lithofacies at seismic scale. The employed methodology relied on stepwise sequential integration of all available data through petrophysical, rock physics analysis and seismic inversion technique. Simultaneous inversion approach is used to invert elastic properties for reservoir interpretation. Seismic-based petrophysical properties are predicted using regression analysis by establishing a functional relationship between well logs for Sembar formation. The rock physics template (acoustic impedance versus Vs/Vs ratio) model helped to differentiate lithological units of sand and shale in the well. Three lithofacies (HC sands, shale and shalier sand) are properly classified in rock physics template, and their probabilities are accurately defined using Bayes' theorem. Finally, estimated lithofacies and hydrocarbon probability map from the Bayesian approach are meticulously validated from well data. The quantitative seismic reservoir characterization study provided important support for the unconventional prospect evaluation and hydrocarbon reserve estimations necessary to delineate unexplored parts which could prove helpful in effectively planning for the horizontal well placement and optimal reservoir development. [ABSTRACT FROM AUTHOR]

What Is Known and Objective?: The latest published guidelines advocate for the area under the concentration‐time curve to minimal inhibitory concentration (AUC0‐24h/MIC) estimated with bayesian calculations. This recommended pharmacokinetic monitoring transition is not based on randomized controlled prospective data. Methods: In this open‐label feasibility RCT, patients were assigned to have their vancomycin dosing adjusted based on bayesian‐guided AUC0‐24h/MIC or trough levels. Primary outcomes were consent rate, number of patients recruited per month, compliance with blood sampling schedule and compliance with bayesian software recommendations. Secondary outcomes focused on target attainment, safety and operational impacts. Results and Discussion: Forty‐five patients underwent randomization (23 bayesian, 22 trough). Consent rate was 37,5% for an average of 9.8 patients recruited per month meeting pre‐specified objectives of 30% (p = 0.073) and 10 (p = 0.74) respectively. A 74.8% compliance with blood sampling schedule was below the pre‐specified objective of 80% (p = 0.038). There was no statistically significant difference between the 83.7% compliance with bayesian software recommendations and the pre‐specified objective of 90% (p = 0.21). Although exploratory, key clinical results were significant increases in the bayesian group for proportion of levels at target (RR 1.32; 95% CI 1.01–1.72; P = 0.038), number of blood samplings for patients (p = 0.036) and pharmacists' time spent on monitoring (p < 0.0001). A tendency towards a reduced incidence of nephrotoxicity in the Bayesian group was observed (RR 0.57; 95% CI 0.16–2.12; p = 0.46). What Is New and Conclusions?: This trial demonstrates that it would be feasible to conduct a properly sized RCT comparing vancomycin Bayesian‐guided AUC0‐24h/MIC to trough level monitoring. Although exploratory, this trial also showed a tendency towards reduced incidence of nephrotoxicity and an increased proportion of dosages at therapeutic targets with Bayesian monitoring. [ABSTRACT FROM AUTHOR]

Design of microwave circuits require extensive simulations, which often take significant computational time due to design complexity. This can be addressed through neural networks (NNs) that provide predictive capability. Predictions often come with uncertainties that need to be quantified. Moreover, optimization and inverse designs are better done using probabilities. This article describes the use of Bayes theorem and machine learning (ML) for solving complex microwave design problems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce an extension of a Brownian bridge with a random length by including uncertainty also in the pinning level of the bridge. The main result of this work is that unlike for deterministic pinning point, the bridge process fails to be Markovian if the pining point distribution is absolutely continuous with respect to the Lebesgue measure. Further results include the derivation of formulae to calculate the conditional expectation of various functions of the random pinning time, the random pinning location, and the future value of the Brownian bridge, given an observation of the underlying process. For the specific case that the pining point has a two-point distribution, we state further properties of the Brownian bridge, e.g. the right continuity of its natural filtration and its semi-martingale decomposition. The newly introduced process can be used to model the flow of information about the behaviour of a gas storage contract holder; concerning whether to inject or withdraw gas at some random future time. [ABSTRACT FROM AUTHOR]

Objectives: To develop further a competing‐risks model for the prediction of a small‐for‐gestational‐age (SGA) neonate by including sonographically estimated fetal weight (EFW) and biomarkers of impaired placentation at 36 weeks' gestation, and to compare the performance of the new model with that of the traditional EFW < 10th percentile cut‐off. Methods: This was a prospective observational study in 29 035 women with a singleton pregnancy undergoing routine ultrasound examination at 35 + 0 to 36 + 6 weeks' gestation. A competing‐risks model for the prediction of a SGA neonate was used. The parameters included in the prior‐history model were provided in previous studies. An interaction continuous model was used for the EFW likelihood. A folded plane regression model was fitted to describe likelihoods of biomarkers of impaired placentation. Stratification plans were also developed. The new model was evaluated and compared with EFW percentile cut‐offs. Results: The performance of the model was better for predicting SGA neonates delivered closer to the point of assessment. The prediction provided by maternal factors alone was improved significantly by the addition of EFW, uterine artery pulsatility index (UtA‐PI) and placental growth factor (PlGF) but not by mean arterial pressure or soluble fms‐like tyrosine kinase‐1. At a 10% false‐positive rate, maternal factors and EFW predicted 77.6% and 65.8% of SGA neonates < 10th percentile delivered before 38 and 42 weeks, respectively. The respective figures for SGA < 3rd percentile were 85.5% and 74.2%. Addition of UtA‐PI and PlGF resulted in marginal improvement in prediction of SGA < 3rd percentile requiring imminent delivery. A competing‐risks approach that combines maternal factors and EFW performed better when compared with fixed EFW percentile cut‐offs at predicting a SGA neonate, especially with increasing time interval between assessment and delivery. The new model was well‐calibrated. Conclusions: A competing‐risks model provides effective risk stratification for a SGA neonate at 35 + 0 to 36 + 6 weeks' gestation and is superior to EFW percentile cut‐offs. The use of biomarkers of impaired placentation in addition to maternal factors and fetal biometry results in small improvement of the predictive performance for a neonate with severe SGA. © 2022 International Society of Ultrasound in Obstetrics and Gynecology. [ABSTRACT FROM AUTHOR]

Age‐at‐death estimation is a difficult task in fragmented or incomplete contexts. The generation and testing of methods are needed to identify their potential application in different types of osteological samples. This paper proposes a new method for age estimation using the first rib. Four hundred and fifteen individuals from the Coimbra Identified Skeletal Collections (Portugal) were studied; the sample was divided in two groups (reference and test samples), used to develop the procedure and to evaluate its reliability. The three rib joints and two variables (surface texture and topography, and periarticular margins and subsidence of the surface) were recorded. Statistical analyses were performed to test the procedure reproducibility, and side and sex variations. Posterior probabilities were calculated following Bayesian statistics and used to estimate the most likely age‐at‐death in the test sample. The results show the reproducibility of the method is adequate, left and right ribs can be indistinctly recorded, and both sexes show similar change patterns. The costal face and the head epiphyseal region offered satisfactory results and the percentages of correct estimations are greater for younger individuals, decreasing as ages‐at‐death are higher. This method is more effective among individuals in the 20–50 years cohort; nevertheless, it also offered satisfactory estimates in older ones. Overestimations in individuals younger than 40 years and underestimations in individuals older than 75 years were identified. In conclusion, the first rib offer adequate estimates when a basic probabilistic approach is chosen. Whenever possible, this proposal must be included in a multifactorial perspective with other age markers. [ABSTRACT FROM AUTHOR]

Objectives: To examine the predictive performance for placental dysfunction related stillbirths of the competing risks model for small‐for‐gestational‐age (SGA) fetuses based on a combination of maternal risk factors, estimated fetal weight (EFW) and uterine artery pulsatility index (UtA‐PI); and second, to compare the performance of this model with that of a stillbirth‐specific model using the same biomarkers and with the Royal College of Obstetricians and Gynecologists (RCOG) guideline for the investigation and management of the SGA fetus. Design: Prospective observational study. Setting: Two UK maternity hospitals. Population: A total of 131 514 women with singleton pregnancies attending for routine ultrasound examination at 19–24 weeks of gestation. Methods: The predictive performance for stillbirth achieved by three models was compared. Main Outcome Measure: Placental dysfunction related stillbirth. Results: At 10% false‐positive rate, the competing risks model predicted 59%, 66% and 71% of placental dysfunction related stillbirths, at any gestation, at <37 weeks and at <32 weeks, respectively, which were similar to the respective figures of 62%, 70% and 73% for the stillbirth‐specific model. At a screen positive rate of 21.8%, as defined by the RCOG guideline, the competing risks model predicted 71%, 76% and 79% of placental dysfunction related stillbirths at any gestation, at <37 weeks and at <32 weeks, respectively, and the respective figures for the RCOG guideline were 40%, 44% and 42%. Conclusion: The predictive performance for placental dysfunction related stillbirths by the competing risks model for SGA was similar to that of the stillbirth‐specific model and superior to that of the RCOG guideline. The competing risks approach for SGA is superior to the RCOG guideline in the prediction of placental dysfunction related stillbirths. The competing risks approach for SGA is superior to the RCOG guideline in the prediction of placental dysfunction related stillbirths. This article includes Author Insights, a video abstract available at: https://vimeo.com/bjogabstracts/authorinsights17066 [ABSTRACT FROM AUTHOR]

The role of quantile functions in modeling various forms of statistical data is well established. Generally classical procedures like method of moments, L-moments, percentiles etc are employed in estimating the parameters of the model. In the present work an attempt is made to infer parameters in the Bayesian framework with special emphasis to distributions in which the quantile functions do not posses tractable distribution functions. The procedure is illustrated for some distributions and real life data. [ABSTRACT FROM AUTHOR]

Background: To account for interindividual variability in the pharmacokinetics (PK) of factor concentrates, PK‐guided dosing is increasingly implemented in haemophilia patients. Calculations are based on provided label potency, but legislation allows a potency difference of ±20% between label and actual potency. It is unknown if these differences affect PK guidance. Aim: Explore the effects of potency differences on individual factor VIII (FVIII) PK parameters and the prediction of FVIII trough levels of dosing regimens. Methods: We analyzed individual preoperative PK profiling data from severe and moderate haemophilia A patients included in the OPTI‐CLOT randomized controlled trial. Label and actual potency were compared, with data on potency provided by pharmaceutical companies. For both potencies, individual PK parameters were estimated and concentration‐time curves were constructed by nonlinear mixed‐effects modelling. Finally, we explored the effect of both the identified and the maximum legislated potency difference on predicted FVIII trough levels infused in a low and high dose regimen. Results: In 45/50 included patients, actual potency was higher than its label potency. The median potency difference was 6.0% (range ‐9.2% to 18.4%) and resulted in varying individual PK parameter estimates but practically identical FVIII concentration‐time curves. As expected, predicted FVIII trough levels were linearly correlated to the actual dose. Conclusion: It is not necessary to take potency differences into account when applying PK guidance of FVIII concentrates in haemophilia A patients. However, when the patient is switched to another FVIII batch after PK‐guided dosing, trough levels may deviate ±20% from calculations based on label dose. [ABSTRACT FROM AUTHOR]

• A concept of comprehensive reliability-based importance measure is proposed. • The interaction between design variables and uncertain parameters is considered. • Probabilistic and nonprobabilistic importance measures are analyzed separately. • An efficient method based on Bayes theorem is applied to calculate the indices. • Examples of multidisciplinary systems show the validity of the proposed method. The traditional reliability-based multidisciplinary design optimization consumes large computation resources due to a large number of design variables and uncertain parameters in multidisciplinary systems. Therefore, the screening of important design variables and uncertain parameters contributes to reducing the difficulty of optimization. However, the current methods ignore the interaction between design variables and uncertain parameters. Via a probabilistic and nonprobabilistic reliability model, the work in this paper establishes the concept of a comprehensive reliability-based importance measure that considers both design variables and uncertain parameters. An efficient method based on the Bayesian theorem is applied to calculate the above-mentioned indices of the proposed importance measure, which can estimate importance measure accurately adopting the same samples with reliability analysis. Therefore, the probabilistic method has faster convergence speed than traditional methods. The nonprobabilitic analysis method can promote the computation efficiency but ensure accuracy when the system performance functions are monotonic. Furthermore, the developed approach contributes to decreasing the time cost of reliability-based optimization in multidisciplinary systems. Additionally, the developed approach can also be applicable to single-disciplinary systems. Moreover, two practical engineering examples and the multidisciplinary design of a hypersonic wing are employed to demonstrate the validity and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Introduction: Systemic juvenile idiopathic arthritis (sJIA) and adult-onset Still's disease (AOSD) represent pediatric and adult variants of the Still's disease continuum. To determine whether clinical outcomes between patients with sJIA and AOSD were similar, Bayesian and population model-based analyses were conducted on endpoints from studies of canakinumab in both patient populations. The objective was to further support the efficacy of canakinumab in patients with AOSD. Methods: A Bayesian analysis of endpoints from a study of canakinumab in AOSD was conducted borrowing information from five pooled sJIA studies using a robust meta-analytic predictive (MAP) approach. Similarity of clinical outcomes across populations was fulfilled if the AOSD study posterior median fell within the 95% predicted credible interval for the outcome of interest, based on the pooled sJIA data. Population model-based analyses (pharmacokinetic [PK] and PK/pharmacodynamic [PKPD]) were conducted to compare the pharmacokinetics and exposure–response relationships between populations. Results: The AOSD study posterior medians for adapted American College of Rheumatology (ACR)30 response, continuous adapted ACR response, number of active joints, C-reactive protein, and absence of fever were within the 95% credible interval for the prediction of the MAP analysis from the pooled sJIA data, supporting the similarity in outcomes between patient populations. PK analysis demonstrated comparable exposure across sJIA age groups and patients with AOSD. PKPD relationships were consistent across patient populations. Analyses indicated that no therapeutic benefit can be expected from a dose increase in patients with AOSD. Conclusion: The analyses presented support the similarity of clinical outcomes following treatment with canakinumab in patients with sJIA and AOSD. [ABSTRACT FROM AUTHOR]

Beaches are thought to be a large reservoir for marine plastics. To protect vulnerable beaches, it is advantageous to have information on the sources of this plastic. Here, we develop a universally applicable Bayesian framework to map sources of plastic arriving on a specific beach. In this framework, we combine Lagrangian backtracking simulations of drifting particles with estimates of plastic input from coastlines, rivers and fisheries. The advantage over traditional Lagrangian simulations is that the Bayesian framework can consider information on known sources, and thus facilitates spatiotemporal source attribution for plastic arriving at the specified beach. We show that the main sources for our target beach in southwest Netherlands are the east coast of the UK, the Dutch coast, the English Channel (fisheries) and the Thames, Seine, Rhine and Trieux (rivers). We also show that floating time is a major uncertainty in source attribution using backtracking. Plain Language Summary: A large part of plastic in the ocean is located at or near beaches. This plastic can break down into micro‐plastics or be ingested by animals. Therefore, it is important to clean up these beaches. The easiest way to do so is to prevent the plastic from entering the oceans initially by interfering at the source. In this study, we develop a framework to find these sources for a given beach. We first simulate the path that plastic has taken to reach this beach. We do this by releasing virtual plastic particles at the beach where they end up. Next, we calculate their paths back in time, computing their trajectories until they reach this beach. We then combine these simulations with data on the sources of plastic: where and when did plastic enter the ocean? We apply this framework to a beach in southwest Netherlands, near the town of Domburg. We quantify seasonal effects, where time‐varying currents cause the plastic to come from different sources. Lastly, we study how plastic sources vary with plastic age (the time between the plastic entering the ocean and beaching at its final location). Key Points: Combined oceanic backtracking and Bayesian statistics supports source attribution of beached plasticStrong seasonal variability in likely sources is found, due to variability in plastic input and currentsFloating time remains a major uncertainty in determining the origin of beached plastic via backtracking [ABSTRACT FROM AUTHOR]

The updated vancomycin guideline and recent studies suggested that trough concentrations may result in underestimation of the actual area under the curve (AUC), leading to excessive dosing and nephrotoxicity. With limited data available on critically ill cancer patients, this study aimed to compare the two methods in this patient population. This was a 5‐year retrospective study on patients treated with vancomycin in the intensive care unit (ICU) of a comprehensive cancer center. The measured trough concentration was compared to Bayesian‐derived AUC/minimum‐inhibitory‐concentration (MIC), considering MIC as 1. Trough concentrations of 15–20 mg/L and AUC of 400–600 mg h/L were considered the targeted goal. Multivariate analysis was performed to identify factors associated with an AUC below the targeted goal. During the study period, 316 patients were included. The mean age was 54 years ±16 (SD); most patients had solid tumors (75%), and 11% had neutropenia. A targeted goal AUC and trough were recorded in 128 (41%) patients and in 64 (20%) patients, respectively. Of the 128 patients with targeted goal AUC, 31 (24%) had targeted goal trough concentrations and 91 (71%) had trough concentrations below 15 mg/L. Furthermore, among the patients with targeted goal trough concentration (n = 64), 33 (52%) had higher than targeted goal AUC. Augmented renal clearance (ARC), defined as a calculated creatinine‐clearance ≥130 ml/min, was associated with an AUC below the targeted goal. In a cohort of critically ill patients with cancer, over two‐thirds of the patients with a targeted goal Bayesian AUC/MIC had trough concentrations below the targeted goal. ARC was associated with AUC below the targeted goal. [ABSTRACT FROM AUTHOR]

The European Union (EU) established the criteria for the classification of shellfish harvesting areas, based on the results of monitoring E. coli in shellfish. The EU also defined E. coli as a microbiological criterion for end product safety, based on a three-class sampling plan. Both criteria are based on the MPN method, a test with a statistical approach and in which different factors contribute to the variability of the outcomes. This theoretical study, based on combinatorics and Bayes' theorem for conditional probability, investigates the consistency between these two criteria and aims to determine the probability of obtaining false compliant and non-compliant results when applying the safety criterion test to shellfish placed on the market. The results show that in the second case, the probability of non-compliant outcomes does not appear negligible within a range between 10% and 50% in the different hypothesized scenarios, with a probability of false noncompliant outcomes over 10%. In addition, the Bayes' Theorem shows that Class A, or Class B areas (as allowed), could be the origin of non-compliant shellfish, with a not negligible frequency. Therefore, within the limits of the assumed working hypotheses, the safety criterion for E. coli, as described in Regulation EC/2073/2005, does not appear to be consistent and coordinated with the classification criteria stated in the Regulation EU/2019/627 and it is not closely related to the sanitary status of shellfish harvesting area. [ABSTRACT FROM AUTHOR]

Objectives: The majority of lung cancer cases are of advanced stage and diagnosis is usually made using minimally invasive small biopsies and cytological specimens. The WHO 2015 classification recommends limiting immunocytochemistry (ICC) to lung cancer typing and molecular testing drives for personalised therapies. An algorithm using Bayes' theorem could be useful for defining antibody profiles. This study aims to assess the impact of different antibody profiles for cytological samples on the accuracy of lung cancer typing with a large‐scale Bayesian analysis. Methods: A retrospective examination of 3419 consecutive smears and/or cytospins diagnosed over 2011‐2016 found 1960 primary lung cancer tumours: 972 adenocarcinomas (ADC), 256 squamous carcinomas (SQC), 268 neuroendocrine tumours (NET), and 464 non‐small cell cancer‐not otherwise specified (NSCC‐NOS). The a priori and a posteriori probabilities, before and after ICC using antibodies singly or in combination, were calculated for different lung cancer types. Results: TTF‐1 or CK7 alone improved the a posteriori probabilities of correct cytological typing for ADC to 86.5% and 95.8%, respectively. For SQC, using p40 (∆Np63) or CK5/6 together with CK5/14 led to comparable results (78.3% and 90.3%). With synaptophysin or CD56 alone, improvements in a posteriori probabilities to 87.5 and 90.3% for the correct recognition of NET could be achieved. Conclusions: Based on morphological and clinical data, the use of two antibodies appears sufficient for reliable detection of the different lung cancer types. This applies to diagnoses that were finalised following ICC both on a clinical or cytological basis and on a histological basis. [ABSTRACT FROM AUTHOR]

Children who experience a traumatic brain injury (TBI) are at elevated risk for a range of negative cognitive and neuropsychological outcomes. Identifying which children are at greatest risk for negative outcomes can be difficult due to the heterogeneity of TBI. To address this barrier, the current study applied a novel method of characterizing brain connectivity networks, Bayesian multi‐subject vector autoregressive modelling (BVAR‐connect), which used white matter integrity as priors to evaluate effective connectivity—the time‐dependent relationship in functional magnetic resonance imaging (fMRI) activity between two brain regions—within the default mode network (DMN). In a prospective longitudinal study, children ages 8–15 years with mild to severe TBI underwent diffusion tensor imaging and resting state fMRI 7 weeks after injury; post‐concussion and anxiety symptoms were assessed 7 months after injury. The goals of this study were to (1) characterize differences in positive effective connectivity of resting‐state DMN circuitry between healthy controls and children with TBI, (2) determine if severity of TBI was associated with differences in DMN connectivity and (3) evaluate whether patterns of DMN effective connectivity predicted persistent post‐concussion symptoms and anxiety. Healthy controls had unique positive connectivity that mostly emerged from the inferior temporal lobes. In contrast, children with TBI had unique effective connectivity among orbitofrontal and parietal regions. These positive orbitofrontal‐parietal DMN effective connectivity patterns also differed by TBI severity and were associated with persisting behavioural outcomes. Effective connectivity may be a sensitive neuroimaging marker of TBI severity as well as a predictor of chronic post‐concussion symptoms and anxiety. [ABSTRACT FROM AUTHOR]

The updated vancomycin guideline and recent studies suggested that trough concentrations may result in underestimation of the actual area under the curve (AUC), leading to excessive dosing and nephrotoxicity. With limited data available on critically ill cancer patients, this study aimed to compare the two methods in this patient population. This was a 5-year retrospective study on patients treated with vancomycin in the intensive care unit (ICU) of a comprehensive cancer center. The measured trough concentration was compared to Bayesian-derived AUC/minimum-inhibitoryconcentration (MIC), considering MIC as 1. Trough concentrations of 15–20 mg/L and AUC of 400–600 mg h/L were considered the targeted goal. Multivariate analysis was performed to identify factors associated with an AUC below the targeted goal. During the study period, 316 patients were included. The mean age was 54 years ±16 (SD); most patients had solid tumors (75%), and 11% had neutropenia. A targeted goal AUC and trough were recorded in 128 (41%) patients and in 64 (20%) patients, respectively. Of the 128 patients with targeted goal AUC, 31 (24%) had targeted goal trough concentrations and 91 (71%) had trough concentrations below 15 mg/L. Furthermore, among the patients with targeted goal trough concentration (n = 64), 33 (52%) had higher than targeted goal AUC. Augmented renal clearance (ARC), defined as a calculated creatinine-clearance ≥130 ml/min, was associated with an AUC below the targeted goal. In a cohort of critically ill patients with cancer, over two-thirds of the patients with a targeted goal Bayesian AUC/MIC had trough concentrations below the targeted goal. ARC was associated with AUC below the targeted goal. [ABSTRACT FROM AUTHOR]

In the South African legal system of fact finding and proof the relevance of an evidentiary fact is not governed by the rules of the law of evidence but by a set of extra-legal principles based on the logic of inferential reasoning and probability theory. However, there is no definitive legal definition, or practical test, of what constitutes relevance in a post-constitutional South African curial context, except for an ambiguous pre-1961 reference to a "blend of common sense, judicial experience and logic, laying outside the law". This article critically evaluates the relationship between relevance and admissibility in the adversarial adjudicative process, with particular reference to the peculiarities of the South African legal system, in which the procedural framework of the fact-finding process has been subjected to a post-apartheid constitutional democracy. In addition, this article provides an interpretative synthesis of prevailing international scholarship in the field, develops a functional three-legged practical relevance test for ease of application by all legal practitioners in the courtroom and provides a uniquely different possible statutory definition of relevance and admissibility. [ABSTRACT FROM AUTHOR]

In diagnostic test evaluation, performance goals are often set for classification accuracy measures such as specificity, sensitivity and diagnostic likelihood ratio. For tests that detect rare conditions, classification accuracy goals are attractive because they can be evaluated in case-control studies enriched for the condition. A neglected area of research is determining classification accuracy goals that confer clinical usefulness of a test. We determine classification accuracy goals based on desired risk stratification, i.e. the post-test risk of having the condition compared with the pre-test risk. We determine goals for rule-out tests, rule-in tests, and those that do both. Goals for negative and positive likelihood ratios (NLR, PLR) are emphasized because of their natural relationships with risk stratification via Bayes Theorem. Goals for specificity and sensitivity are implied by goals on NLR and PLR. Goals that confer superiority or non-inferiority of a test to a comparator are based on approximating risk differences and relative risks by functions of likelihood ratios. Inference is based on Wald confidence intervals for ratios of likelihood ratios. To illustrate, we consider hypothetical data on a fetal fibronectin assay for ruling out risk of pre-term birth and two human papillomavirus assays for detecting cervical cancer. ClinicalTrials.gov identifier: NCT01931566. [ABSTRACT FROM AUTHOR]

This paper presents a probabilistic framework for estimating the time-variant seismic fragility of high-rise buildings under carbonation-induced corrosion with a multiple damage model. A 42-story steel frame RC core tube (SFRCT) building was selected as a representative high-rise building to illustrate the proposed method. The main novelties of this study are that (1) a Bayesian updating method is used to determine the posterior probability distributions of the unknown parameters in the time-varying demand models of the maximum interstory drift and the residual drift at the top floor, (2) an analytical derivation method is used to compute the probability distribution of the corrosion initiation time in reinforcements, (3) the uncertainties associated with the corrosion initiation time and unknown parameters in demand models are incorporated into fragility estimates based on total probability theory, and (4) the elliptical copula and Archimedean copula are used to develop bivariate fragility estimates that consider the correlations among the limit states associated with maximum drifts and residual drifts, respectively. The results indicate that the seismic demand and fragility estimates of this high-rise building increase as the corrosion initiation time, which highlights the necessity of accounting for the uncertainty in the corrosion initiation time. In addition, the Gaussian copula and the Clayton copula (special cases of the elliptical copula and the Archimedean copula, respectively) produce similar joint probability distributions for the two-dimensional limit states; however, the Clayton copula can be used in a more general condition. The proposed methodology is applicable for evaluating the seismic life-cycle fragility of both existing and new high-rise buildings under multicriteria performance indicators. [ABSTRACT FROM AUTHOR]

The treatment of uncertainty in climate-change science is dominated by the far-reaching influence of the 'frequentist' tradition in statistics, which interprets uncertainty in terms of sampling statistics and emphasizes p-values and statistical significance. This is the normative standard in the journals where most climate-change science is published. Yet a sampling distribution is not always meaningful (there is only one planet Earth). Moreover, scientific statements about climate change are hypotheses, and the frequentist tradition has no way of expressing the uncertainty of a hypothesis. As a result, in climate-change science, there is generally a disconnect between physical reasoning and statistical practice. This paper explores how the frequentist statistical methods used in climate-change science can be embedded within the more general framework of probability theory, which is based on very simple logical principles. In this way, the physical reasoning represented in scientific hypotheses, which underpins climate-change science, can be brought into statistical practice in a transparent and logically rigorous way. The principles are illustrated through three examples of controversial scientific topics: the alleged global warming hiatus, Arctic-midlatitude linkages, and extreme event attribution. These examples show how the principles can be applied, in order to develop better scientific practice. "La théorie des probabilités n'est que le bon sens reduit au calcul." (Pierre-Simon Laplace, Essai Philosophiques sur les Probabilités, 1819). "It is sometimes considered a paradox that the answer depends not only on the observations but on the question; it should be a platitude." (Harold Jeffreys, Theory of Probability, 1st edition, 1939). [ABSTRACT FROM AUTHOR]

In the presentation, problems have been addressed in perception and popular delusions in the interpretation of information related to the level of confidence / uncertainty of tests by which the presence or absence of contamination is verified. The theoretical essence of the probabilities of a false positive and false negative result, as well as the level of confidence / uncertainty of the test results and on this basis there are assumptions about the perception of such information and possible misconceptions in its interpretation by society. Assumptions made are checked on the information based on a non-reporting survey. The results obtained reveal user difficulties in rationalizing and interpreting the data provided as well as when detecting logical links characteristic of such information. [ABSTRACT FROM AUTHOR]

Null hypothesis significance testing (NHST) and p‐values are widespread in the cardiac surgical literature but are frequently misunderstood and misused. The purpose of the review is to discuss major disadvantages of p‐values and suggest alternatives. We describe diagnostic tests, the prosecutor's fallacy in the courtroom, and NHST, which involve inter‐related conditional probabilities, to help clarify the meaning of p‐values, and discuss the enormous sampling variability, or unreliability, of p‐values. Finally, we use a cardiac surgical database and simulations to explore further issues involving p‐values. In clinical studies, p‐values provide a poor summary of the observed treatment effect, whereas the three‐number summary provided by effect estimates and confidence intervals is more informative and minimizes over‐interpretation of a "significant" result. p‐values are an unreliable measure of the strength of evidence; if used at all they give only, at best, a very rough guide to decision making. Researchers should adopt Open Science practices to improve the trustworthiness of research and, where possible, use estimation (three‐number summaries) or other better techniques. [ABSTRACT FROM AUTHOR]

Background: Among patients with diabetes and chronic coronary disease, it is unclear if invasive management improves outcomes when added to medical therapy.Methods: The ISCHEMIA (International Study of Comparative Health Effectiveness with Medical and Invasive Approaches) trials (ie, ISCHEMIA and ISCHEMIA-Chronic Kidney Disease) randomized chronic coronary disease patients to an invasive (medical therapy + angiography and revascularization if feasible) or a conservative approach (medical therapy alone with revascularization if medical therapy failed). Cohorts were combined after no trial-specific effects were observed. Diabetes was defined by history, hemoglobin A1c ≥6.5%, or use of glucose-lowering medication. The primary outcome was all-cause death or myocardial infarction (MI). Heterogeneity of effect of invasive management on death or MI was evaluated using a Bayesian approach to protect against random high or low estimates of treatment effect for patients with versus without diabetes and for diabetes subgroups of clinical (female sex and insulin use) and anatomic features (coronary artery disease severity or left ventricular function).Results: Of 5900 participants with complete baseline data, the median age was 64 years (interquartile range, 57-70), 24% were female, and the median estimated glomerular filtration was 80 mL·min-1·1.73-2 (interquartile range, 64-95). Among the 2553 (43%) of participants with diabetes, the median percent hemoglobin A1c was 7% (interquartile range, 7-8), and 30% were insulin-treated. Participants with diabetes had a 49% increased hazard of death or MI (hazard ratio, 1.49 [95% CI, 1.31-1.70]; P<0.001). At median 3.1-year follow-up the adjusted event-free survival was 0.54 (95% bootstrapped CI, 0.48-0.60) and 0.66 (95% bootstrapped CI, 0.61-0.71) for patients with diabetes versus without diabetes, respectively, with a 12% (95% bootstrapped CI, 4%-20%) absolute decrease in event-free survival among participants with diabetes. Female and male patients with insulin-treated diabetes had an adjusted event-free survival of 0.52 (95% bootstrapped CI, 0.42-0.56) and 0.49 (95% bootstrapped CI, 0.42-0.56), respectively. There was no difference in death or MI between strategies for patients with diabetes versus without diabetes, or for clinical (female sex or insulin use) or anatomic features (coronary artery disease severity or left ventricular function) of patients with diabetes.Conclusions: Despite higher risk for death or MI, chronic coronary disease patients with diabetes did not derive incremental benefit from routine invasive management compared with initial medical therapy alone. Registration: URL: https://www.clinicaltrials.gov; Unique identifier: NCT01471522. [ABSTRACT FROM AUTHOR]

Background: Postherpetic neuralgia (PHN) is a refractory complication of herpes zoster (HZ). To prevent PHN, various strategies have been aggressively adopted. However, the efficacy of these strategies remains controversial. Therefore, we aimed to estimate the relative efficacy of various strategies used in clinical practice for preventing PHN using a network meta-analysis (NMA). Methods: We performed a systematic and comprehensive search to identify all randomized controlled trials. The primary outcome was the incidence of PHN at 3 months after acute HZ. We performed both frequentist and Bayesian NMA and used the surface under the cumulative ranking curve (SUCRA) values to rank the interventions evaluated. Results: In total, 39 studies were included in the systematic review and NMA. According to the SUCRA value, the incidence of PHN was lower in the order of continuous epidural block with local anesthetics and steroids (EPI-LSE), antiviral agents with subcutaneous injection of local anesthetics and steroids (AV + sLS), antiviral agents with intracutaenous injection of local anesthetics and steroids (AV + iLS) at 3 months after acute HZ. EPI-LSE, AV + sLS and AV + iLS were also effective in preventing PHN at 1 month after acute HZ. And paravertebral block combined with antiviral and antiepileptic agents was effective in preventing PHN at 1, 3, and 6 months. Conclusions: The continuous epidural block with local anesthetics and steroid, antiviral agents with intracutaneous or subcutaneous injection of local anesthetics and a steroid, and paravertebral block combined with antiviral and antiepileptic agents are effective in preventing PHN. [ABSTRACT FROM AUTHOR]

The concern over x-ray exposure risks can overshadow the potential benefit of radiography, especially in cases where manual therapy is employed. Spinal malalignment cannot be accurately visualized without imaging. Manual therapy and the load tolerances of injured spinal tissues raise different criteria for the use of x-rays for spinal disorders than in medical practice. Current regulatory bodies rely on radiography risk assessments based on Linear-No-Threshold (LNT) risk models. There is a need to consider radiography guidelines for chiropractic which are different from those for medical practice. Radiography practice guidelines are summaries dominated by frequentist interpretations in the analysis of data from studies. In contrast, clinicians often employ a pseudo-Bayesian form of reasoning during the clinical decision-making process. The overrepresentation of frequentist perspectives in evidence-based practice guidelines alter decision-making away from practical assessment of a patient's needs, toward an overly cautious standard applied to patients without regard to their risk/benefit likelihoods relating to radiography. Guidelines for radiography in chiropractic to fully assess the condition of the spine and spinal alignment prior to manual therapy, especially with high velocity, low amplitude spinal manipulation (HVLA-SM), should necessarily differ from those used in medical practice. [ABSTRACT FROM AUTHOR]

Uveitis encompasses a heterogeneous group of clinical entities with the common feature of intraocular inflammation. In addition to patient history and examination, a focused set of laboratory investigations is frequently necessary to establish a specific diagnosis. There is limited consensus among uveitis specialists regarding appropriate laboratory evaluation for many distinct patient presentations. The appropriateness of a laboratory test for a given case of uveitis will depend on patient-specific as well as epidemiologic factors. Bayesian analysis is a widely used framework for the interpretation of laboratory testing, but is seldom adhered to in clinical practice. Bayes theorem states that the predictive value of a particular laboratory test depends on the sensitivity and specificity of that test, as well as the prevalence of disease in the population being tested. In this review we will summarize the performance of commonly-utilized laboratory tests for uveitis, as well as the prevalence of uveitic diagnoses in different geographic practice settings. We will propose a logical framework for effective laboratory testing in uveitic disease through rigorous application of Bayesian analysis. Finally, we will demonstrate that while many highly sensitive laboratory tests offer an effective means to rule out associated systemic disease, limited test specificity and low pretest probability often preclude the diagnosis of systemic disease association with any high degree of certainty, even in the face of positive testing. [ABSTRACT FROM AUTHOR]

Gamma spectrometry is a passive nondestructive assay method used to quantify radionuclides present in nuclear objects. Basic methods using empirical calibration with a standard to quantify the activity of nuclear materials by determining the calibration coefficient are ineffective on nonreproducible nuclear objects such as waste packages. Package specifications such as composition or geometry change from one package to another and exhibit large variability of objects. The current standard quantification process uses numerical modeling of the measured scene with few available data such as geometry or composition, in particular density, material, screen, geometric shape, matrix composition, matrix, and source distribution. Some of them are strongly dependent on package data knowledge and operator backgrounds. The French Atomic Energy Commission (CEA) is developing a methodology to quantify nuclear materials in waste packages and waste drums without operator adjustment and internal package configuration knowledge. This method suggests combining a stochastic approach which uses, among others, surrogate models available to simulate the gamma attenuation behavior, a Bayesian approach considering conditional probability densities and prior information of problem inputs, and Markov Chain Monte Carlo (MCMC) algorithms which solve inverse problems, with gamma ray emission radionuclide spectra, and the outside dimensions of the objects of interest. The methodology has been tested to quantify actinide activity with a low bulk density matrix, weakly attenuating compositions, without information on the distribution of the source in terms of actinide masses and materials composing the drums. Activity uncertainties are taken into account. [ABSTRACT FROM AUTHOR]

Background: Multicentre RCTs are widely used by critical care researchers to answer important clinical questions. However, few trials evaluating mortality outcomes report statistically significant results. We hypothesised that the low proportion of trials reporting statistically significant differences for mortality outcomes is plausibly explained by lower-than-expected effect sizes combined with a low proportion of participants who could realistically benefit from studied interventions.Methods: We reviewed multicentre trials in critical care published over a 10-yr period in the New England Journal of Medicine, the Journal of the American Medical Association, and the Lancet. To test our hypothesis, we analysed the results using a Bayesian model to investigate the relationship between the proportion of effective interventions and the proportion of statistically significant results for prior distributions of effect size and trial participant susceptibility.Results: Five of 54 trials (9.3%) reported a significant difference in mortality between the control and the intervention groups. The median expected and observed differences in absolute mortality were 8.0% and 2.0%, respectively. Our modelling shows that, across trials, a lower-than-expected effect size combined with a low proportion of potentially susceptible participants is consistent with the observed proportion of trials reporting significant differences even when most interventions are effective.Conclusions: When designing clinical trials, researchers most likely overestimate true population effect sizes for critical care interventions. Bayesian modelling demonstrates that that it is not necessarily the case that most studied interventions lack efficacy. In fact, it is plausible that many studied interventions have clinically important effects that are missed. [ABSTRACT FROM AUTHOR]

Summary: Nowadays, the bulk of Internet traffic uses Transmission Control Protocol (TCP) for reliable transmission. But the standard TCP's performance is very poor in high‐speed networks (HSNs), and hence, the core gigabytes links are usually underutilization. This problem has roots in conservative nature of TCP, especially in its Additive Increase Multiplicative Decrease (AIMD) phase. In other words, since TCP cannot figure out precisely the congestion status of the network, it follows a conservative strategy to keep the network from overwhelming. We believe that precisely congestion estimation in the network can solve this problem by avoiding unnecessary conservation. To this end, this paper proposes an algorithm, which considers packet loss and delay information jointly and employs a probabilistic approach to accurately estimation of congestion status in the network. To examine the proposed scheme performance, extensive simulations have been performed in the NS‐2 environment. Simulation results reveal that the proposed algorithm has better performance than existing algorithms in terms of bottleneck utilization, stability, throughput, and fairness. [ABSTRACT FROM AUTHOR]

A method of personality recognition by echographic parameters of the human ear is developed based on the naive Bayes classifier in the two following modes: the biometric identification (EER = 0.0053) and the biometric authentication (FRR = 0.0002 at FAR ≤ 0.0001), respectively. A device is developed for recording the biometric characteristics of the external ear, and a set of echographic data is collected from the external ears of 75 subjects. The spectral and cepstral characteristics of the signals reflected from the ear canal are used as biometric parameters. Several window functions for constructing spectra and cepstrograms are considered. It is established that more than 90% of "cepstral" features have a weak correlation, which allows us to use the naive Bayesian classifier and to obtain highly accurate results of user recognition at the same time. An advantage of the Bayesian classification is the possibility of the robust fast learning of the identification system. [ABSTRACT FROM AUTHOR]