Search

The prostate-specific antigen (PSA) threshold of 3.0 ng/ml is obsolete because it yields a test that is too sensitive. Urologists should adopt a more nuanced approach that accounts for benign prostate hypertrophy. This includes the use of magnetic resonance imaging, PSA density, and age-specific PSA reference ranges.

Key Points Question Is the Primary Care Posttraumatic Stress Disorder (PTSD) Screen, as revised for the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PC-PTSD-5), diagnostically accurate and acceptable to Veterans Affairs primary care patients? Findings In this diagnostic study of 396 primary care–seeking veterans, the 5-item PC-PTSD-5 was both diagnostically accurate and acceptable to participants. A cut point score of 4 best balanced the false negatives and false positives, although women may require a lower cut point (ie, a score of 3). Meaning Like its predecessor, the PC-PTSD-5 is an effective and efficient tool for PTSD screening in Veterans Affairs primary care clinics., This diagnostic study examines whether the Primary Care Posttraumatic Stress Disorder (PTSD) screen for the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (DSM-5) is a diagnostically accurate and acceptable measure for use in Veterans Affairs primary care clinics., Importance Posttraumatic stress disorder (PTSD) is a serious mental health disorder that can be effectively treated with empirically based practices. PTSD screening is essential for identifying undetected cases and providing patients with appropriate care. Objective To determine whether the Primary Care PTSD screen for the Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (PC-PTSD-5) is a diagnostically accurate and acceptable measure for use in Veterans Affairs (VA) primary care clinics. Design, Setting, and Participants This cross-sectional, diagnostic accuracy study enrolled participants from May 19, 2017, to September 26, 2018. Participants were recruited from primary care clinics across 2 VA Medical Centers. Session 1 was conducted in person, and session 2 was completed within 30 days via telephone. A consecutive sample of 1594 veterans, aged 18 years or older, who were scheduled for a primary care visit was recruited. Data analysis was performed from March 2019 to August 2020. Exposures In session 1, participants completed a battery of questionnaires. In session 2, a research assistant administered the PC-PTSD-5 to participants, and then a clinician assessor blind to PC-PTSD-5 results conducted a structured diagnostic interview for PTSD. Main Outcomes and Measures The range of PC-PTSD-5 cut points overall and across gender was assessed, and diagnostic performance was evaluated by calculating weighted κ values. Results In total, 495 of 1594 veterans (31%) participated, and 396 completed all measures and were included in the analyses. Participants were demographically similar to the VA primary care population (mean [SD] age, 61.4 [15.5] years; age range, 21-93 years) and were predominantly male (333 participants [84.1%]) and White (296 of 394 participants [75.1%]). The PC-PTSD-5 had high levels of diagnostic accuracy for the overall sample (area under the receiver operating characteristic curve [AUC], 0.927; 95% CI, 0.896-0.959), men (AUC, 0.932; 95% CI, 0.894-0.969), and women (AUC, 0.899, 95% CI, 0.824-0.974). A cut point of 4 ideally balanced false negatives and false positives for the overall sample and for men. However, for women, this cut point resulted in high numbers of false negatives (6 veterans [33.3%]). A cut point of 3 fit better for women, despite increasing the number of false positives. Participants rated the PC-PTSD-5 as highly acceptable. Conclusions and Relevance The PC-PTSD-5 is an accurate and acceptable screening tool for use in VA primary care settings. Because performance parameters will change according to sample, clinicians should consider sample characteristics and screening purposes when selecting a cut point.

The management of datasets containing heterogeneous types of data is a crucial point in the context of precision medicine, where genetic, environmental, and life-style information of each individual has to be analyzed simultaneously. Clustering represents a powerful method, used in data mining, for extracting new useful knowledge from unlabeled datasets. Clustering methods are essentially distance-based, since they measure the similarity (or the distance) between two elements or one element and the cluster centroid. However, the selection of the distance metric is not a trivial task: it could influence the clustering results and, thus, the extracted information. In this study we analyze the impact of four similarity measures (Manhattan or L1 distance, Euclidean or L2 distance, Chebyshev or L∞ distance and Gower distance) on the clustering results obtained for datasets containing different types of variables. We applied hierarchical clustering combined with an automatic cut point selection method to six datasets publicly available on the UCI Repository. Four different clusterizations were obtained for every dataset (one for each distance) and were analyzed in terms of number of clusters, number of elements in each cluster, and cluster centroids. Our results showed that changing the distance metric produces substantial modifications in the obtained clusters. This behavior is particularly evident for datasets containing heterogeneous variables. Thus, the choice of the distance measure should not be done a-priori but evaluated according to the set of data to be analyzed and the task to be accomplished.

The amount of physical activity reported using accelerometry can vary depending on the method used. This study examined variability in four different methods of calculating moderate to vigorous physical activity (MVPA) among older adults, as well as lifestyle correlates of physical activity. The MVPA data were captured (n = 111; Mage = 70.3 years, SDage = 6.3) using waist-worn ActiGraph wGT3X-BT monitors and examined using 10-min bouted versus sporadic methods, and with cut points calibrated to older and younger adults. The sample, on average, did not meet national guidelines of 150 min/week of MVPA when using bouted methods, irrespective of cut point used. This was not the case for sporadic MVPA. More physical activity was reported for participants with two or more physical hobbies, but no association with social behavior was found. These results demonstrate the wide variability possible in reporting methods for accelerometry data and their relation to adherence rates for national health recommendations.

Positron emission tomography (PET) imaging with 2-deoxy-2-[18F]-fluorodeoxyglucose (FDG) was proposed as prognostic marker in radiotherapy. Various uptake metrics and cut points were used, potentially leading to inflated effect estimates. Here, we performed a meta-analysis and systematic review of the prognostic value of pretreatment FDG–PET in head and neck squamous cell carcinoma (HNSCC) and non-small cell lung cancer (NSCLC), with tests for publication bias. Hazard ratio (HR) for overall survival (OS), disease free survival (DFS), and local control was extracted or derived from the 57 studies included. Test for publication bias was performed, and the number of statistical tests and cut-point optimizations were registered. Eggers regression related to correlation of SUVmax with OS/DFS yielded p = 0.08/p = 0.02 for HNSCC and p < 0.001/p = 0.014 for NSCLC. No outcomes showed significant correlation with SUVmax, when adjusting for publication bias effect, whereas all four showed a correlation in the conventional meta-analysis. The number of statistical tests and cut points were high with no indication of improvement over time. Our analysis showed significant evidence of publication bias leading to inflated estimates of the prognostic value of SUVmax. We suggest that improved management of these complexities, including predefined statistical analysis plans, are critical for a reliable assessment of FDG–PET.

Background: Reliability of body composition measurement techniques is essential to the accurate reporting of intervention outcomes. However, the between-day precision error of commonly used techniques, as well as the reference multi-compartment model, in a population-representative sample are currently unknown. Objectives: To quantify technical and biological precision error of body composition techniques in comparison to the referent 4-compartment (4C) model. Methods: Men and women (1:1 ratio; 18-85 years old; n = 90) completed 2 consecutive-day body composition testing sessions, including individual components of the referent 4C model. Testing was undertaken in accordance with best practice guidance for each technique, including standardized presentation and a consistent time of day. Repeat measurements were conducted on day 1 for technical precision, and between-day measurements were conducted for biological precision quantification. Results: On average, all measurements met acceptable error limits and presented typically low technical and biological error [

Aims To examine third trimester fasting venous plasma glucose (FVPG) according to the distribution of a Danish population of pregnant women and identify potential local FVPG thresholds for GDM diagnosis related to risks of adverse pregnancy outcomes. Methods In the observational Odense Child Cohort (OCC) study, 1516 women had FVPG measured at 27–28 weeks’ gestation and were considered normal by Danish criteria and remained untreated. Maternal FVPG from OCC were standardized according to the local FVPG mean and standard deviation calibrated to data from the Hyperglycemia and Adverse Pregnancy Outcome (HAPO) study. Associations between maternal FVPG and clinical and anthropometric outcomes were analysed. Potential FVPG cut points were identified. Results Unadjusted areas under the ROC curve for FVPG to discriminate for large for gestational age (LGA) and hypertensive disorders of pregnancy were 0.61 (95% CI 0.56, 0.67) and 0.57 (95% CI 0.52, 0.63), respectively. The Youden FVPG cut point for LGA was 5.5 mmol/L and 5.0 mmol/L for hypertensive disorders of pregnancy. Conclusions This study identified a potential locally appropriate third trimester FVPG cut point between 5.5 and 5.7 mmol/L based on LGA risk in pregnancy. This cut point should be validated prospectively in other Danish cohorts.

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or with artificial intelligence (AI), allows radiologists to improve diagnoses that may improve patient treatment. Purpose To evaluate whether the diagnostic performance of radiologists in the differentiation of cancer from noncancer at dynamic contrast material-enhanced (DCE) breast MRI is improved when using an AI system compared with conventionally available software. Materials and Methods In a retrospective clinical reader study, images from breast DCE MRI examinations were interpreted by 19 breast imaging radiologists from eight academic and 11 private practices. Readers interpreted each examination twice. In the "first read," they were provided with conventionally available computer-aided evaluation software, including kinetic maps. In the "second read," they were also provided with AI analytics through computer-aided diagnosis software. Reader diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of distinguishing between malignant and benign lesions. The primary study end point was the difference in AUC between the first-read and the second-read conditions. Results One hundred eleven women (mean age, 52 years ± 13 [standard deviation]) were evaluated with a total of 111 breast DCE MRI examinations (54 malignant and 57 nonmalignant lesions). The average AUC of all readers improved from 0.71 to 0.76 (P = .04) when using the AI system. The average sensitivity improved when Breast Imaging Reporting and Data System (BI-RADS) category 3 was used as the cut point (from 90% to 94%; 95% confidence interval [CI] for the change: 0.8%, 7.4%) but not when using BI-RADS category 4a (from 80% to 85%; 95% CI: -0.9%, 11%). The average specificity showed no difference when using either BI-RADS category 4a or category 3 as the cut point (52% and 52% [95% CI: -7.3%, 6.0%], and from 29% to 28% [95% CI: -6.4%, 4.3%], respectively). Conclusion Use of an artificial intelligence system improves radiologists' performance in the task of differentiating benign and malignant MRI breast lesions. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Krupinski in this issue.

Discovery of biomarkers has been steadily increasing over the past decade. Although a plethora of biomarkers has been reported in the biomedical literature, few have been sufficiently validated for broader clinical applications. One particular challenge that may have hindered the adoption of biomarkers into practice is the lack of reproducible biomarker cut points. In this article, we attempt to identify some common statistical issues related to biomarker cut point identification and provide guidance on proper evaluation, interpretation, and validation of such cut points. First, we illustrate how discretization of a continuous biomarker using sample percentiles results in significant information loss and should be avoided. Second, we review the popular "minimal-P-value" approach for cut point identification and show that this method results in highly unstable P values and unduly increases the chance of significant findings when the biomarker is not associated with outcome. Third, we critically review a common analysis strategy by which the selected biomarker cut point is used to categorize patients into different risk categories and then the difference in survival curves among these risk groups in the same dataset is claimed as the evidence supporting the biomarker's prognostic strength. We show that this method yields an exaggerated P value and overestimates the prognostic impact of the biomarker. We illustrate that the degree of the optimistic bias increases with the number of variables being considered in a risk model. Finally, we discuss methods to appropriately ascertain the additional prognostic contribution of the new biomarker in disease settings where standard prognostic factors already exist. Throughout the article, we use real examples in oncology to highlight relevant methodologic issues, and when appropriate, we use simulations to illustrate more abstract statistical concepts.

Distance learning has become a popular phenomenon across the world during the COVID-19 pandemic. This led to answer copying behavior among individuals. The cut point of the Kullback-Leibler Divergence (KL) method, one of the copy detecting methods, was calculated using the Youden Index, Cost-Benefit, and Min Score p-value approaches. Using the cut point obtained, individuals were classified as a copier or not, and the KL method was examined for cases where the determination power of the KL method was 1000, and 3000 sample size, 40 test length, copiers' rate was 0.05 and 0.15, and copying percentage was 0.1, 0.3 and 0.6. As a result, when the cut point was obtained with the Min Score p-value approach, one of the cutting methods approaches, it was seen that the power of the KL index to detect copier was high under all conditions. Similarly, under all conditions, it was observed that the second method, in which the detection power of the KL method was high, was the Youden Index approach. When the sample size and the copiers' rate increased, it was observed that the power of the KL method decreased when the cut point with the cost-benefit approach was used.

Objectives: To determine the optimal number of examined lymph nodes (ELNs) and examined node stations (ENSs) in patients with radiologically pure-solid NSCLC and to investigate the impact of ELNs and ENSs on accurate staging and long-term survival. Methods: Data from six institutions in the People’s Republic of China on resected c-stage Ⅰ to Ⅱ NSCLCs presenting as pure-solid tumors were analyzed for the impact of ELNs and ENSs on nodal upstaging, stage migration, recurrence-free survival, and overall survival by using multivariate models. The correlations between different end points and ELNs or ENSs were fitted with a smoother (using Locally Weighted Scatterplot Smoothing tool), and the structural break points were determined by the Chow test. Results: Both ELNs and ENSs were identified as prognostic factors for overall survival (ENS: hazard ratio [HR], 0.697; 95% confidence interval [CI]: 0.590–0.824; p < 0.001; ELN: HR, 0.945; 95% CI: 0.909–0.983; p = 0.005) and recurrence-free survival (ENS: HR, 0.863; 95% CI: 0.791–0.941; p = 0.001; ELN: HR, 0.960; 95% CI: 0.938–0.981; p < 0.001). Intraoperative ELNs and ENSs were found to be associated with postoperative nodal upstaging. Cut point analysis revealed an optimal cutoff of 16 LNs and five node stations for patients with c-stage Ⅰ to Ⅱ pure-solid NSCLCs, which were examined in our multi-institutional cohort. Conclusions: Both ELNs and ENSs are associated with more accurate node staging and better long-term survival. We recommend 16 LNs and five stations as the cut point for evaluating the quality of LN examination for c-stage Ⅰ to Ⅱ patients with radiologically pure-solid NSCLCs.

A multivariate longitudinal DCM is developed that is the composite of two components, the log-linear cognitive diagnostic model (LCDM) as the measurement model component that evaluates the mastery status of attributes at each measurement occasion, and a generalized multivariate growth curve model that describes the growth of each attribute over time. The proposed model represents an improvement in the current longitudinal DCMs given its ability to incorporate both balanced and unbalanced data and to measure the growth of a single attribute directly without assuming that attributes grow in the same pattern. One simulation study was conducted to evaluate the proposed model in terms of the convergence rates, the accuracy of classification, and parameter recoveries under different combinations of four design factors: the sample size, the growth patterns, the G matrix design, and the number of measurement occasions. The results revealed the following: (1) In general, the proposed model provided good convergence rates under different conditions. (2) Regarding the classification accuracy, the proposed model achieved good recoveries on the probabilities of attribute mastery. However, the correct classification rates depended on the cut point that was used to classify individuals. For individuals who truly mastered the attributes, the correct classification rates increased as the measurement occasions increased; however, for individuals who truly did not master the attributes, the correct classification rates decreased slightly as the numbers of measurement occasions increased. Cohen's kappa increased as the number of measurement occasions increased. (3) Both the intercept and main effect parameters in the LCDM were recovered well. The interaction effect parameters had a relatively large bias under the condition with a small sample size and fewer measurement occasions; however, the recoveries were improved as the sample size and the number of measurement occasions increased. (4) Overall, the proposed model achieved acceptable recoveries on both the fixed and random effects in the generalized growth curve model.

The American Statistical Association (ASA) published a statement in 2016 in The American Statistician for “researchers, practitioners and science writers who are not primarily statisticians” on the proper use and interpretation of p-values. Three years later, the ASA acknowledged that telling researchers what not to do with p-values was insufficient. Consequently, 3 years later an open access, special issue appeared with 43 papers proposing various novel and sophisticated alternatives to classical p-values for use with scientific methods in the twenty-first century. In the opening remarks, the editors stated that “no p-value can reveal the plausibility, presence, truth, or importance of an association or effect” and banned statistical significance: “don’t say it, don’t use it.” This paper questions both statements with a simulated data study. It is shown that p-values are strongly related to correlation coefficients under a true null hypothesis; hence, can reveal the “importance of an association or effect.” Furthermore, it demonstrates why a cut point for statistical significance is still a viable, ancillary tool for assessing the substantive significance of statistical effects with small sample sizes (n

We show that for an arbitrarily given closed Riemannian manifold $M$ admitting a point $p \in M$ with a single cut point, every closed Riemannian manifold $N$ admitting a point $q \in N$ with a single cut point is diffeomorphic to $M$ if the radial curvatures of $N$ at $q$ are sufficiently close in the sense of $L^1$-norm to those of $M$ at $p$. Our result hence not only produces a weak version of the Cartan--Ambrose--Hicks theorem in the case where underlying manifolds admit a point with a single cut point, but also is a kind of a weak version of the Blaschke conjecture for spheres proved by Berger. In particular that result generalizes one of theorems in Cheeger's Ph.D. Thesis in that case. Remark that every exotic sphere of dimension $> 4$ admits a metric such that there is a point whose cut locus consists of a single point., 16 page, minor corrections and revision

In prognostic/diagnostic medical research, it is often the goal to identify a biomarker that differentiates between patients with and without a condition, or patients that will have good or poor response to a given treatment. The statistical literature is abundant with methods for evaluating single biomarkers for these purposes. However, in practice, a single biomarker rarely captures all aspects of a disease process; therefore, it is often the case that using a combination of biomarkers will improve discriminatory ability. A variety of methods have been developed for combining biomarkers based on the maximization of some global measure or cost-function. These methods usually create a score based on a linear combination of the biomarkers, upon which the standard single biomarker methodologies (such as the Youden's index) are applied. However, these single biomarker methodologies do not account for the multivariable nature of the combined biomarker score. In this article we present generalized inference and bootstrap approaches to estimating confidence intervals for the Youden's index and corresponding cut-point for a combined biomarker. These methods account for inherent dependencies and provide accurate and efficient estimates. A simulation study and real-world example utilize data from a Duchene Muscular Dystrophy study are also presented.

ObjectiveTo determine whether the ABC/2 method could accurately and reliably measure infarct volume and guide thrombectomy decision in acute stroke cases presenting with late or unknown onset.MethodsFour physicians who routinely use MRI for acute stroke imaging, blinded to the RAPID results, measured the diffusion-weighted imaging (DWI) infarct volume using the ABC/2 method. Measurements with ABC/2 (the index test) were compared with RAPID (the reference standard) to calculate sensitivity, specificity, and accuracy measures for various volume cutpoints. Thrombectomy decisions based on RAPID and raters' measurements using the criteria from the Diffusion-Weighted Imaging or Computerized Tomography Perfusion Assessment With Clinical Mismatch in the Triage of Wake-Up and Late-Presenting Strokes Undergoing Neurointervention With Trevo (DAWN) trial criteria were compared. Interrater and intrarater agreement was measured using kappa statistics.ResultsAccuracy with the ABC/2 method was greater than 80% for each rater and each volume cut point. Interrater and intrarater agreement was substantial to excellent for each volume cut point. Treatment decisions with ABC/2 volume estimations showed strong interrater and intrarater agreement, and led to similar thrombectomy decisions compared with RAPID in more than 85% of cases.ConclusionDWI infarct volume measurement using ABC/2 method shows strong accuracy and reliability and may be an acceptable alternative to RAPID software for the application of DAWN criteria for thrombectomy decision-making.

Aim: Competitive inhibition with excess unlabeled drug is used to confirm the presence of antidrug antibodies (ADA) in study samples. We evaluated specific and nonspecific responses from both drug-naive and drug-treated subjects to identify conditions required by the confirmatory assay to make accurate ADA classifications. Results: Nonspecific signal measured in drug-naive samples used to determine assay cut points was uniformly low and close to the screening cut point. Confirmatory assays performed on incurred study samples with nonspecific responses significantly above the level observed during cut point determination resulted in incorrect ADA classifications. Conclusion: Intensity of confirmatory response should be proportional to the screening response and therefore, to ensure accurate ADA classifications, the confirmatory responses cannot be considered as independent but need to be evaluated in relation to the screening responses.

Midlife women experience elevated risk for cardiovascular disease and often receive advice to increase physical activity to mitigate this risk. Use of accelerometers to measure ambulatory physical activity requires selection of appropriate thresholds for estimating moderate-to-vigorous physical activity (MVPA), and choice of cut points may lead to meaningfully different conclusions about midlife women's physical activity (PA) engagement. This is particularly important given the recent elimination of 10-minute bout requirements for MVPA. This two-phase study examined differences between four cut point methods among midlife women with cardiovascular disease (CVD) risk. We used findings from Study 1 (exploratory) to generate hypotheses for Study 2 (confirmatory).Across studies, participants (N = 65) were midlife women with an additional CVD risk factor (eg, hypertension). Participants wore waistband accelerometers for seven days. Daily totals were calculated for minutes in light and MVPA using four common quantification methods (Freedson, Matthews, Swartz, and Troiano).Multilevel models showed meaningful differences between methods (P 0.0001). For total (non-bouted) minutes of MVPA, Freedson and Troiano methods showed that participants barely met MVPA recommendations (30 min per day), whereas Matthews and Swartz methods showed that participants greatly exceeded this goal. As differences between methods were smaller using MVPA bouts of 10 minutes or more (though remained significant), the observed variation was due in part to small bursts of MVPA dispersed throughout the day.Findings demonstrate the need for careful consideration of PA quantification among midlife women with CVD risk, and for further investigation to determine the most appropriate quantification method. : Video Summary:http://links.lww.com/MENO/A545.Video Summary:http://links.lww.com/MENO/A545.

In this paper, we prove that if X is a local dendrite different from the circle and $$f:X\rightarrow X$$ is a continuous map then f is pointwise recurrent if and only if it is a homeomorphism such that every cut point is periodic. Meanwhile, we show in the case of dendrites that the set of almost periodic points is a continuum and equals to the union of all $$\omega $$-limit sets provided that the omega limit map is continuous and we show that it is only a necessary condition for the continuity of the omega limit map. We end the paper by suggesting some new open problems.

Adequate adjustment to bodily changes during various phases of cancer treatment is important to patients’ emotional well-being. The Body Image Scale (BIS) is a widely used tool for assessment of body image concerns in different cancer types. However, a cut point score indicative of clinically relevant body image concerns has not been established. The purpose of our study was to evaluate whether the previously suggested, but not validated, BIS cut point score of ≥ 10 is an adequate indicator of psychological distress. In a prospective cross-sectional study, 590 adult patients were recruited from a psychiatric oncology clinic (November 2017–March 2018). Patient-reported body image concerns, depression, anxiety, and emotional distress were assessed with the BIS, Patient Health Questionnaire-9, Generalized Anxiety Disorder Scale-7, and National Comprehensive Cancer Network Distress Thermometer, respectively. Almost half of the patients had a BIS score ≥ 10; these were more likely to be younger, female, Hispanic, and to have breast cancer than patients with a score

We introduce the binacox, a prognostic method to deal with the problem of detect- ing multiple cut-points per features in a multivariate setting where a large number of continuous features are available. The method is based on the Cox model and com- bines one-hot encoding with the binarsity penalty, which uses total-variation regular- ization together with an extra linear constraint, and enables feature selection. Original nonasymptotic oracle inequalities for prediction (in terms of Kullback-Leibler diver- gence) and estimation with a fast rate of convergence are established. The statistical performance of the method is examined in an extensive Monte Carlo simulation study, and then illustrated on three publicly available genetic cancer datasets. On these high- dimensional datasets, our proposed method signi cantly outperforms state-of-the-art survival models regarding risk prediction in terms of the C-index, with a computing time orders of magnitude faster. In addition, it provides powerful interpretability from a clinical perspective by automatically pinpointing signi cant cut-points in relevant variables.

Introduction: Tumor mutational burden (TMB) is a quan-titative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole-exome sequencing (wesTMB) and gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research have jointly addressed the need for harmonization among TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment. Methods: A total of 20 samples from three tumor types (lung adenocarcinoma, head and neck squamous cell car-cinoma, and colon adenocarcinoma) with available WES data were analyzed for psTMB using six panels across 15 testing centers. Interlaboratory and interplatform variation, including agreement on variant calling and TMB classifica-tion, were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated. Results: Sixteen samples had low interlaboratory and interpanel psTMB variation, with 87.7% of pairwise com-parisons revealing a Spearman & rsquo;s r greater than 0.6. A wesTMB cut point of 199 missense mutations projected to psTMB cut points between 7.8 and 12.6 mutations per megabase pair; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cut points of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation arti-facts, DNA input, sequencing depth, genome coverage, and variant allele frequency cut points. Conclusions: This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important param-eters for reliable tissue TMB assessment that require careful control. As complex or composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and Friends of Cancer Research also delineate a general framework and blueprint for the eval-uation of such assays. (C) 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.

In view of the shortcomings of traditional wireless sensor and actuator networks, such as low speed and high consumption, this paper studies the repair algorithm of wireless sensor and actuator networks based on graph theory. Firstly, the concept of wireless sensor and actuator network is analyzed, then the cooperative algorithm of wireless sensor and actuator network is elaborated, and a SA cooperative algorithm with low energy consumption and low latency is proposed. Finally, the algorithm of cut-point detection is summarized. The algorithm is mainly based on simulation model NS2 and topological repair of cut point. The simulation results show that the real-time performance and energy consumption of the proposed algorithm are better than those of K-IDS algorithm and RCR algorithm.

There is no universally agreed upon definition for ultrafine particles (UFP). Commonly used definitions for UFP are either particle number below 100 nm or total particle number, but without an agreed upon lower cut point. For example, a lower cut point of 3 nm compared to 10 nm could result in a substantially higher count. Another definition for UFP is total particle mass but without a commonly agreed upon aerodynamic diameter upper cut point, e.g., below 100 nm, 200 nm, 300 nm, etc. Yet another definition is lung deposited surface area weighted by lung deposition fraction, found mainly in the particle mobility diameter range from 20 to 400 nm. It is clear from these definitions that there are inconsistencies in the way UFP is used and defined in the literature. Sometimes these metrics are well correlated, sometimes not. In this paper we suggest three exposure metrics: UFP-N, UFP-M, and UFP-S, that we believe will add clarity. These metrics represent total number, mass, and surface area below 500 nm, respectively. For surface area and mass, the 500 nm cut point can be either aerodynamic or mobility diameter depending upon measurement methodology. For all metrics, this cut point captures nearly all of the primary particle emissions from mobile sources. Furthermore, UFP-N would include a lower cut point of 3–6 nm and would not require an upper size cut point because there is very little particle number above 500 nm or even above 100 nm. Thus, our definition of UFP-N is consistent with the current definition of ultrafine number except for, importantly, the specification of a lower cut point. These exposure metrics can help facilitate consistency in the characterization of both short- and long-term UFP ambient exposures and associated health effects in epidemiological studies.

The Beck Depression Inventory revised (BDI-II) is widely used tool to screen for depression. The aim of the present study was to systematically review and synthesize studies that determined optimal cut points for the BDI-II. We identified 27 studies that tried to identify optimal cut points for the BDI-II. Study quality was assessed using QUADAS criteria. Cut points and their variability were analyzed descriptively, via simulation and synthesized with a diagnostic meta-analysis. Analysis was performed on all studies and subgroups based on the setting (psychiatric, somatic, healthy). Cut points identified as optimal ranged from 10 to 25 across all studies. Simulation-based estimations of the variability inherent in studies show that much of the between-study differences may be attributed to random fluctuations. Diagnostic meta-analysis across all studies revealed that a cut point of 14.5 (95% CI 12.75–16.44) is optimal, yielding a sensitivity of 0.86 and a specificity of 0.78. Analyses within the different settings suggest using sample-specific cut points, specifically 18.18 in psychiatric settings, and 12.9 in primary care settings and healthy populations. Most studies aimed at determining optimal cut points fail to acknowledge that reported results are only estimates and subject to random fluctuations resulting in conflicting recommendations for practitioners. Taking into account these fluctuations, we find that practitioners should use different cut points to screen for depression in primary care and healthy populations (a score of 13 and higher indicates depression) and psychiatric settings (a score of 19 and higher indicates depression). Methods to describe this variability and meta-analysis to synthesize findings across studies should be used more widely.

A density partition curve for a hypothetical steady-state separator is applied to a known feed density distribution to give the density distributions of the product and reject. Density fractionation of each of these streams is then simulated, with the fractionator Ecarte Probable, EpX, set at a specific level, to produce a set of seven or more fractions of varying mass and increasing average density. This study then describes a new algorithm that attempts to recover the partition curve of the original steady-state separator, using only the three sets of limited fractionation data and the assumption that the form of the partition curve equation is known. The algorithm first uses a simple interpolation rule to convert each set of fractionation data into a cumulative density distribution. Then the feed density distribution and the partition curve parameters are simultaneously adjusted until a consistent set of feed, product and reject density distributions is found with minimum variation from the raw fractionation data. The algorithm was applied to a simple rectangular feed distribution, and then a more realistic distribution. In both cases the algorithm accurately determined the density cut point (D50) of the separator, even for poor quality fractionations. The accuracy of the determined separator Ep value depended on the fractionator EpX and the amount of near-density material. For the simple rectangular distribution, the algorithm under predicted the separator Ep, with the error being about 34% of the fractionator EpX. For the more realistic feed distribution, there was more scatter in the Ep values, but still the same general trend. The error increased when there was little near-density material. Increasing the number of flow fractions from 7 to 11 brought some improvement in accuracy. However, above 11 fractions there was no further significant improvement. Expressing the partition function in terms of D75 and D25 (instead of D50 and Ep) reduced the sensitivity of the algorithm to the initial guess values.

The antibody seroprevalence of young stock can be a useful indicator of recent or current infection in a herd. We examine the factors that contribute to the assessment of herd exposure to disease, via spot testing for antibody, using bovine viral diarrhoea virus (BVDv) as an example. A statistical distribution of seroprevalences for BVDv in beef herds identified three groups of herds: low, intermediate and high within-herd BVDv antibody seroprevalence. We tested two assumptions –the intermediate seroprevalence group of herds is assumed to be negative for BVDv at the herd level and alternatively if this group is assumed to be positive. We found that: The herd-level sensitivity and specificity are sensitive to the assumption regarding the herds with intermediate seroprevalence. If an appropriate cut-point is chosen, reducing the sample size from ten to five does not produce a large drop in herd-level test performance. Increasing the cut-point may be valuable at the outset of an eradication programme. Increasing the sample size and decreasing the cut-point is advantageous towards the end of an eradication programme, to minimise the risk of positive herds being misclassified. The framework presented here illustrates how seroprevalence screening may be understood and assessed.

The concept of cut point convex sets is used to study non-cut points of a connected topological space. Some relations between cut point convex sets, H-sets and COTS are established. We prove that an H-set of a COTS is contained in a COTS with endpoints a and b for some a, b in the H-set. It is shown that if a connected topological space X has at most two non-cut points and an R ( i ) set that contains all the closed cut points of X, then X is a COTS with endpoints. Further we show that if every non-degenerate proper regular closed connected subset of a connected topological space X contains only finitely many closed points of X, then X has at least two non-cut points. A characterization of a non-indiscrete finite connected subspace of the Khalimsky line is also obtained.

Both invasive and non-invasive parameters have been reported for assessment of the physiological status of the coronary circulation. Fractional flow reserve and coronary (or myocardial) flow reserve may be obtained by invasive or non-invasive means. These metrics of coronary stenosis severity have achieved wide clinical acceptance for guiding revascularization decisions and risk stratification. Other indices are obtained invasively (e.g., instantaneous wave-free ratio, iFR; hyperemic stenosis resistance) or non-invasively (e.g., PET absolute myocardial blood flow (mL/min/g)) and have been used for the same purposes. Both iFR, and whole-cycle distal coronary to aortic mean pressure (Pd/Pa) are measured under basal condition and used for assessment of hemodynamic stenosis severity as is index of basal stenosis resistance (BSR). These metrics typically are dichotomized at an empirically derived cut point into "normal" and "abnormal" categories for purposes of clinical decision making and data analysis. Once dichotomized the indices do not always point in the same direction and so confusion may arise. This review, therefore, will present basic principles relevant to understanding commonly employed metrics of the physiological status of the coronary circulation, potential strengths and weaknesses, and hopefully an improved appreciation of the clinical information provided by each.

Methods: Based on the index S (S = SENSITIVITY (SEN) × SPECIFICITY (SPE)), the new weighted product index Sw is defined as Sw = (SEN)2w × (SPE)2(1-w), where (0≤w≤1). The Sw is developed to be a new...

The Liebowitz Social Anxiety Scale (LSAS) is the most frequently used instrument to assess social anxiety disorder (SAD) in clinical research and practice. Both a self-reported (LSAS-SR) and a clinician-administered (LSAS-CA) version are available. The aim of the present study was to define optimal cut-off (OC) scores for remission and response to treatment for the LSAS in a German sample. Data of N = 311 patients with SAD were used who had completed psychotherapeutic treatment within a multicentre randomized controlled trial. Diagnosis of SAD and reduction in symptom severity according to the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition, served as gold standard. OCs yielding the best balance between sensitivity and specificity were determined using receiver operating characteristics. The variability of the resulting OCs was estimated by nonparametric bootstrapping. Using diagnosis of SAD (present vs. absent) as a criterion, results for remission indicated cut-off values of 35 for the LSAS-SR and 30 for the LSAS-CA, with acceptable sensitivity (LSAS-SR: .83, LSAS-CA: .88) and specificity (LSAS-SR: .82, LSAS-CA: .87). For detection of response to treatment, assessed by a 1-point reduction in the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition, rating, a reduction of 28% for the LSAS-SR and 29% for the LSAS-CA yielded the best balance between sensitivity (LSAS-SR: .75, LSAS-CA: .83) and specificity (LSAS-SR: .76, LSAS-CA: .80). To our knowledge, we are the first to define cut points for the LSAS in a German sample. Overall, the cut points for remission and response corroborate previously reported cut points, now building on a broader data basis.

Currently, human motion analysis using three-dimensional (3D) data creates closer awareness in computer vision with the introduction of cost-effective Kinect or other depth cameras. This study attempts to segment a continuous 3D skeletal sequence into several disjointed sub-sequences, each of which is corresponding to a complete action. To address this issue, the authors propose a supervised time-series segmentation algorithm. A bidirectional propagation search scheme is employed to reach a solution. Specifically, a human skeleton is formulated as a point in multidimensional space, and a motion trajectory is further represented as a sequence. Each training action sequence serves as an atom in a dictionary, which is adopted by an l 2 - regularised collaborative representation classifier. Based on the fact that the reconstruction error of the collaborative representation measures the similarity between a test sub-sequence and training sequences, they utilise its variation over time to capture action transition. Cut point detection and sub-sequence recognition are simultaneously achieved. Experiments on the authors' recorded 3D skeletal sequences demonstrate that the proposed algorithm outperforms existing human motion segmentation techniques. Their algorithm is capable of extending to segment various dimensional sequences. This extensibility is validated by synthetic signal segmentation experiments.

Introduction: Patients in India frequently present with prostatic surface antigen (PSA) report and request for prostatic biopsy to rule out malignancy. With fear of harboring malignancy set in patient's mind, it becomes difficult to counsel them about absolute indications and need of biopsy. Whether serum PSA has same predictability in symptomatic patients in the Indian context for advising prostatic biopsy at same reference ranges as in western countries, remains to be answered. Materials and Methods: Symptomatic patients between 45 and 70 years of age presenting with either raised serum PSA (>4 ng/ml) reports or abnormal digital rectal examination (DRE) were considered as cases. Standard 12 core transrectal ultrasound-guided prostatic biopsy was done. Statistical analysis using optimal cut points, an R package was done to overview different PSA cut points for the recommendation of prostatic biopsy. Results: A total of 534 patients were included. Mean age was 64 years. Malignancy was detected in total 77 patients (14.42%). Malignancy was identified in 3.59% (10/279) and 30% (63/210) patients at serum PSA ranges 4–10 ng/ml and serum PSA >10 ng/ml, respectively. Both, maximum sensitivity and specificity were found at PSA cut point 9.7 ng/ml. We evaluated these patients to identify the PSA cut point above which unnecessary biopsies will be avoided. We kept power of study maximum, i.e., 1 with confidence interval of 0.95. Conclusion: PSA value 9.7 ng/ml should be considered as the cut point above which prostatic biopsy should be done to avoid unnecessary biopsies. Unless accompanied by abnormal DRE finding at PSA range 4–10 ng/ml, morbidity of prostatic biopsy procedure can be avoided using this cut-point.

To identify a clinically meaningful cut-point for the single item dry mouth question of the MD Anderson Symptom Inventory-Head and Neck module (MDASI-HN).Head and neck cancer survivors who had received radiation therapy (RT) completed the MDASI-HN, the University of Michigan Hospital Xerostomia Questionnaire (XQ), and the health visual analog scale (VAS) of the EuroQol Five Dimension Questionnaire (EQ-5D). The Bayesian information criteria (BIC) were used to test the prediction power of each tool for EQ-5D VAS. The modified Breiman recursive partitioning analysis (RPA) was used to identify a cut point of the MDASI-HN dry mouth score (MDASI-HN-DM) with EQ-5D VAS, using a ROC-based approach; regression analysis was used to confirm the threshold effect size.Two-hundred seven respondents formed the cohort. Median follow-up from the end of RT to questionnaire completion was 88 months. The single item MDASI-HN-DM score showed a linear relationship with the XQ composite score (ρ = 0.80, p 0.001). The MDASI-HN-DM displayed improved model performance for association with EQ-5D VAS as compared to XQ (BIC of 1803.7 vs. 2016.9, respectively). RPA showed that an MDASI-HN-DM score of ≥6 correlated with EQ-5D VAS decline (LogWorth 5.5).The single item MDASI-HN-DM correlated with the multi-item XQ and performed favorably in the prediction of QOL. A MDASI-HN-DM cut point of ≥6 correlated with decline in QOL.

Background There is growing interest in computed tomography (CT) measures of skeletal muscle cross-sectional area (CSA) for nutrition assessment. Multiple software programs are available, but little work has been done comparing programs. We aimed to determine if CT-derived measures of skeletal muscle CSA at the level of the L3 are influenced by the software program used. We also demonstrate the importance of the ImageJ corrigendum published in this journal. Methods Two software programs, National Institutes of Health ImageJ and Tomovision sliceOmatic, were compared. ImageJ measures were obtained using both the original tutorial and corrigendum instructions. Skeletal muscle CSA at the level of the L3 was measured in advanced heart failure and head and neck cancer populations by 3 different investigators. Intraclass correlation coefficients were used to calculate intrarater and interrater reliability. Bland-Altman analysis was used to assess agreement. Results Both software programs yielded excellent intrarater and interrater reliability scores (intraclass correlation coefficients, 0.985-1.000). The overall mean difference (ImageJ tutorial with corrigendum - sliceOmatic) for the entire sample (N = 51) was found to be 1.53 cm2 (95% CI, 0.59-2.47 cm2 ). The overall mean difference (ImageJ corrected - original) for the entire sample (N = 51) was found to be -11.35 cm2 (95% CI, -12.75 to -9.95 cm2 ). Conclusion Measures of skeletal muscle CSA at the L3 were found to be ∼1.53 cm2 higher with ImageJ than sliceOmatic. This difference was not found to affect interpretation against a published cut point. The importance of accounting for the ImageJ tutorial corrigendum was shown to be clinically significant when applied to published cut points.

In their analysis of 451 consecutive adults with nontraumatic, spontaneous intracranial hemorrhage (ICH), Dr. Faigle et al. derived and internally validated a simple score for predicting critical care needs. This INTRINSIC score was also externally validated in a separate population and was found to have high sensitivity and specificity for identifying whether a patient with acute ICH may require transfer to an intensive care unit. Dr. Patel et al. comment on the limitations of scoring systems such as this, with an outcome of “intensive care interventions” being a subjective endpoint. The investigators acknowledge that interinstitutional practice variability may limit the transportability of their findings; however, the model was externally validated in a center remote from their own practice. Dr. Foroush et al. note that the estimated positive likelihood ratios (PLRs) for each cut point in the INTRINSIC score render it less useful at higher cut points. Dr. Faigle recognizes that high PLRs may not be possible for individual cut points and reference several examples of scoring systems (e.g., RACE score and LAMS), which are presently in use for stroke triage which do not meet the PLR standards cited by Dr. Foroush et al. Examining the cut point of individual scores may not provide as meaningful a measure as compared with a continuous composite score. On a separate note, readers may find that the score has considerable overlap with the traditional ICH score, which is predictive of early mortality. Both scores assign points for ICH volume, presence of intraventricular hemorrhage, and Glasgow Coma Scale. However, the INTRINSIC score is unique in that it may prove useful for early triage of patients with unclear dispositions after hospital arrival. In their analysis of 451 consecutive adults with nontraumatic, spontaneous intracranial hemorrhage (ICH), Dr. Faigle et al. derived and internally validated a simple score for predicting critical care needs. This INTRINSIC score was also externally validated in a separate population and was found to have high sensitivity and specificity for identifying whether a patient with acute ICH may require transfer to an intensive care unit. Dr. Patel et al. comment on the limitations of scoring systems such as this, with an outcome of “intensive care interventions” being a subjective endpoint. The investigators acknowledge that interinstitutional practice variability may limit the transportability of their findings; however, the model was externally validated in a center remote from their own practice. Dr. Foroush et al. note that the estimated positive likelihood ratios (PLRs) for each cut point in the INTRINSIC score render it less useful at higher cut points. Dr. Faigle recognizes that high PLRs may not be possible for individual cut points and reference several examples of scoring systems (e.g., RACE score and LAMS), which are presently in use for stroke triage which do not meet the PLR standards cited by Dr. Foroush et al. Examining the cut point of individual scores may not provide as meaningful a measure as compared with a continuous composite score. On a separate note, readers may find that the score has considerable overlap with the traditional ICH score, which is predictive of early mortality. Both scores assign points for ICH volume, presence of intraventricular hemorrhage, and Glasgow Coma Scale. However, the INTRINSIC score is unique in that it may prove useful for early triage of patients with unclear dispositions after hospital arrival.

The aim of this study was to unravel the methodological challenges when exploring nutritional inadequacy, involving 608 healthy pregnant women. The usual intake of twenty-one nutrients was recorded by employing a validated FFQ. Simulated datasets of usual intake were generated, with randomly imposed uncertainty. The comparison between the usual intake and the EAR was accomplished with the probability approach and the EAR cut-point method. Point estimates were accompanied by bootstrap confidence intervals. Bootstrap intervals applied on the risk of inadequacy for raw and simulated data tended in most cases to overlap. A detailed statistical analysis, aiming to predict the level of inadequacy, as well as the application of the EAR cut-point method, along with bootstrap intervals, could effectively be used to assess nutrient inadequacy. However, the final decision for the method used depends on the distribution of nutrient-intake under evaluation. Irrespective of the applied methodology, moderate to high levels of inadequacy, calculated from FFQ were identified for certain nutrients (e.g., vitamins C, B6, magnesium, vitamin A), while the highest were recorded for folate and iron. Considering that micronutrient-poor, obesogenic diets are becoming more common, the underlying rationale may help towards unraveling the complexity characterizing nutritional inadequacies, especially in vulnerable populations.

Background Studies have found associations between increasing BMIs and the development of various chronic health conditions. The BMI cut points, or thresholds beyond which comorbidity incidence can be accurately detected, are unknown. Objective The aim of this study is to identify whether BMI cut points exist for 11 obesity-related comorbidities. Methods US adults aged 18-75 years who had ≥3 health care visits at an academic medical center from 2008 to 2016 were identified from eHealth records. Pregnant patients, patients with cancer, and patients who had undergone bariatric surgery were excluded. Quantile regression, with BMI as the outcome, was used to evaluate the associations between BMI and disease incidence. A comorbidity was determined to have a cut point if the area under the receiver operating curve was >0.6. The cut point was defined as the BMI value that maximized the Youden index. Results We included 243,332 patients in the study cohort. The mean age and BMI were 46.8 (SD 15.3) years and 29.1 kg/m2, respectively. We found statistically significant associations between increasing BMIs and the incidence of all comorbidities except anxiety and cerebrovascular disease. Cut points were identified for hyperlipidemia (27.1 kg/m2), coronary artery disease (27.7 kg/m2), hypertension (28.4 kg/m2), osteoarthritis (28.7 kg/m2), obstructive sleep apnea (30.1 kg/m2), and type 2 diabetes (30.9 kg/m2). Conclusions The BMI cut points that accurately predicted the risks of developing 6 obesity-related comorbidities occurred when patients were overweight or barely met the criteria for class 1 obesity. Further studies using national, longitudinal data are needed to determine whether screening guidelines for appropriate comorbidities may need to be revised.