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Sampling from large dataset is commonly used in the frequent patterns (FPs) mining. To tightly and theoretically guarantee the quality of the FPs obtained from samples, current methods theoretically stabilize the supports of all the patterns in random samples, despite only FPs do matter, so they always overestimate the sample size. We propose an algorithm called multiple sampling-based FPs mining (MSFP). The MSFP first generates the set of approximate frequent items (AFI), and uses the AFI to form the set of approximate FPs without supports ( AFP*), where it does not stabilize the value of any item's or pattern's support, but only stabilizes the relationship ≥ or < between the support and the minimum support, so the MSFP can use small samples to successively obtain the AFI and AFP*, and can successively prune the patterns not contained by the AFI and not in the AFP*. Then, the MSFP introduces the Bayesian statistics to only stabilize the values of supports of AFP*'s patterns. If a pattern's support in the original dataset is unknown, the MSFP regards it as random, and keeps updating its distribution by its approximations obtained from the samples taken in the progressive sampling, so the error probability can be bound better. Furthermore, to reduce the I/O processes in the progressive sampling, the MSFP stores a large enough random sample in memory in advance. The experiments show that the MSFP is reliable and efficient.

As a powerful tool for synthesizing information from multiple studies, meta-analysis has gained high popularity in many disciplines. Conclusions stemming from meta-analyses are often used to direct theory development, calibrate sample size planning, and guide critical decision-making and policymaking. However, meta-analyses can be conflicted, misleading, and irreproducible. One of the reasons for meta-analyses to be misleading is the improper handling of measurement unreliability. We show that even when there is no publication bias, the current meta-analysis procedures would frequently detect nonexistent effects, and provide severely biased estimates and intervals with coverage rates far below the intended level. In this study, an effective approach to correcting for unreliability is proposed and evaluated via simulation studies. Its sensitivity to the violation of the homogeneous reliability and residual correlation assumption is also tested. The proposed method is illustrated using a real meta-analysis on the relationship between extroversion and subjective well-being. Substantial differences in meta-analytic results are observed between the proposed method and existing methods. Further, although not specifically designed for aggregating effect sizes with various measures, the proposed method can be used to fulfill the purpose. The study ends with discussions on the limitations and guidelines for implementing the proposed approach. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

This paper studies second-order properties of the many instruments robust t -ratios based on the limited information maximum likelihood and Fuller estimators for instrumental variable regression models with homoskedastic errors under the many instruments asymptotics, where the number of instruments may increase proportionally with the sample size n , and proposes second-order refinements to the t -ratios to improve the size and power properties. Based on asymptotic expansions of the null and non-null distributions of the t -ratios derived under the many instruments asymptotics, we show that the second-order terms of those expansions may have non-trivial impacts on the size as well as the power properties. Furthermore, we propose adjusted t -ratios whose approximation errors for the null rejection probabilities are of order O ( n − 1 ) in contrast to the ones for the unadjusted t -ratios of order O ( n − 1 / 2 ) , and show that these adjustments induce some desirable power properties in terms of the local maximinity. Although these results are derived under homoskedastic errors, we also establish a stochastic expansion for a heteroskedasticity robust t -ratio, and propose an analogous adjustment under slight deviations from homoskedasticity.

Structural equation modeling (SEM) is a widespread approach to test substantive hypotheses in psychology and other social sciences. However, most studies involving structural equation models neither report statistical power analysis as a criterion for sample size planning nor evaluate the achieved power of the performed tests. In this tutorial, we provide a step-by-step illustration of how a priori, post hoc, and compromise power analyses can be conducted for a range of different SEM applications. Using illustrative examples and the R package semPower, we demonstrate power analyses for hypotheses regarding overall model fit, global model comparisons, particular individual model parameters, and differences in multigroup contexts (such as in tests of measurement invariance). We encourage researchers to yield reliable-and thus more replicable-results based on thoughtful sample size planning, especially if small or medium-sized effects are expected. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Summary Objectives The vocal tract discomfort scale is a self-rating seven-point Likert scale that quantifies frequency and severity of eight qualitative descriptors including burning, tight, dry, aching, tickling, sore, irritable, and lump in the throat, and ranges from 0 (never/none) to 6 (always/extreme; Mathieson et al. 2009). The objectives of the current study were to compare the vocal tract discomfort scale results between elementary school teachers and high school teachers and between male and female teachers using the Persian vocal tract discomfort scale. Also, teachers in different age ranges and with different experiences were compared regarding vocal tract discomfort symptoms. Methods The researchers chose 20 elementary and high schools by simple random sampling in Khorramabad, Iran. The survey was given to available teachers of the selected schools. Considering the inclusion criteria, required sample size, and after excluding questionnaires that were not correctly answered, 120 were selected such that 30 were chosen for each subgroup. Subjects consisted of 60 elementary school teachers (30 females and 30 males) with the mean age of 40.92 years (standard deviation = 6.07) and 60 high school teachers (30 females and 30 males) with the mean age of 40.67 years (standard deviation = 6.00). SPSS 25 was used for analyzing the data. Results Results indicated that the frequency and severity of the vocal tract discomfort in elementary school teachers were significantly higher than for the high school teachers with a medium to large effect size. Although the frequency and severity of the symptoms were higher in female compared with male teachers, those differences were not significantly different. Younger teachers had lower frequency and severity ratings of vocal tract discomfort symptoms than older teachers. Teaching experience was not an important factor in predicting vocal tract discomfort symptoms in teachers. Conclusions The results of this study suggest that there is higher frequency, greater severity, and higher percentages of vocal tract discomfort symptoms in elementary compared with high school teachers. In addition, although the mean frequency and severity of vocal tract discomfort symptoms were not significantly different between females and males, females reported higher percentages of the symptoms. Because each of the eight vocal tract symptoms was experienced at the time of testing by between 42% (tightness) and 68% (dryness) of the participants, it is suggested that an educational program regarding vocal tract discomfort may be helpful for this profession.

COVID-19 is a worldwide public health threat. Diagnosis by RT-PCR has been employed as the standard method to confirm viral infection. Sample pooling testing can optimize the resources by reducing the workload and reagents shortage, and be useful in laboratories and countries with limited resources. This study aims to evaluate SARS-CoV-2 detection by sample pooling testing in comparison with individual sample testing.We created 210 pools out of 245 samples, varying from 4 to 10 samples per pool, each containing a positive sample. We conducted detection of SARS-CoV-2-specific RdRp/E target sites.Pooling of three samples for SARS-CoV-2 detection might be an efficient strategy to perform without losing RT-PCR sensitivity.Considering the positivity rate in Dominican Republic and that larger sample pools have higher probabilities of obtaining false negative results, the optimal sample size to perform a pooling strategy shall be three samples.La COVID-19 es una amenaza de salud pública mundial. La RT-PCR es el método estándar para confirmar la infección. La estrategia de pruebas de muestras agrupadas puede reducir la carga de trabajo y la escasez de reactivos, y ser útil en países con escasos recursos. Evaluamos la detección del SARS-CoV-2 mediante esta estrategia en comparación con pruebas individuales.Creamos 210 grupos de 245 muestras, de 4 a 10 muestras por grupo, cada uno con una muestra positiva. Realizamos extracción de ARN y qRT-PCR para detectar la presencia de la diana RdRp/E.La combinación de hasta 3 muestras para la detección del SARS-CoV-2 podría ser una estrategia eficaz sin perder la sensibilidad.Considerando la tasa de positividad en República Dominicana y que los grupos con más muestras tienen mayor probabilidad de obtener resultados falsos negativos, el tamaño óptimo para realizar esta estrategia es de 3 muestras.

Background Course of Alcohol Dependence Syndrome (ADS) is studded with multiple relapses. Personality factors are implicated as one of the influencing factors in the course of this disorder. Keeping in view of scarcity of Indian data available, the study was planned with the aim to find Personality traits more commonly associated with ADS patients, and identify specific traits, associated with relapses of ADS. Method With sample size of 100 consecutive cases and 100 controls, socio-demographic data was collected. Alcohol Use Disorder Identification Test (AUDIT) and Severity of Alcohol Use Disorder Test (SAD-Q) were administered to each of these patient. Personality dimensions were assessed with NEO-five factor inventory (NEO-FFI) (Costa and McCrae), for both groups and further compared for differences in the dimensions in each of its subscale. Results The NEO scores, showed statistically significant difference with Cases having higher scores in Neuroticism and control group in Openness and Conscientiousness. Neuroticism linked to higher scores of AUDIT and SADQ and also associated with relapses. Other traits also showed statistically significant association which are discussed. Conclusion As new factors are being explored for effective management, routine personality profiling is easily accomplished and can give delightful insight into focused and designed management plan.

Summary Introduction Vocal cord vibration after transoral CO2 LASER-guided thyroarytenoid (TA) myoneurectomy in adductor spasmodic dysphonia (AdSD) patients is unclear to date. The precise vibratory patterns in AdSD patients are difficult to evaluate with routine videolaryngostroboscopy. High-speed videolaryngoscopy (HSV) is an ideal choice to evaluate such patients. This study was performed to compare pre- and postoperative, after 6 months, vocal fold vibratory onset delay (VFVOD) and closed phase glottal cycle (CPGC) in AdSD patients following transoral CO2 LASER-guided TA myoneurectomy using the HSV. Materials and methods Retrospective study, conducted from January, 2016 to January, 2019, of the AdSD patients who underwent transoral CO2 LASER-guided TA myoneurectomy using the HSV. Patient data were acquired from the hospital database to evaluate VFVOD and CPGC from HSV recordings of the patients. VFVOD was calculated as sum of prephonatory delay (PPD) and steady-state delay (SSD). The PPD and SSD were evaluated and compared separately for each patient. The MedCal Version 19.2.6 was used for data analysis. Paired sample t test was performed to compute the significance of the difference between the mean of the dataset. A P value less than 0.05 was considered significant. Results A total of nine patients were included in the study, out of which three were females and six were males. The average age was 45.5 ± 6.9 years. The mean of postoperative PPD (166.8 ± 22.1), SSD (76.5 ± 8.6), and CPGC (62.6 ± 4.8) were significantly less than mean of preoperative PPD (222.6 ± 22.1), SSD (97.7 ± 9.5), and CPGC (71.6 ± 5 %), with P values of 0.0007, 0.0001, and 0.0001, respectively. Conclusions There was a significant decrease in VFVOD and CPGC posttransoral CO2 LASER-guided TA myoneurectomy in AdSD patients after 6 months follow-up. This study also establishes efficiency of the HSV to measure the vocal cord vibration in the patients with AdSD. The primary limitations of the study were the small sample size and its retrospective nature. Future prospective studies with increased sample size can further substantiate the findings of the work performed here.

Newton-step approximations to pseudo maximum likelihood estimates of spatial autoregressive models with a large number of parameters are examined, in the sense that the parameter space grows slowly as a function of sample size. These have the same asymptotic efficiency properties as maximum likelihood under Gaussianity but are of closed form. Hence they are computationally simple and free from compactness assumptions, thereby avoiding two notorious pitfalls of implicitly defined estimates of large spatial autoregressions. For an initial least squares estimate, the Newton step can also lead to weaker regularity conditions for a central limit theorem than those extant in the literature. A simulation study demonstrates excellent finite sample gains from Newton iterations, especially in large multiparameter models for which grid search is costly. A small empirical illustration shows improvements in estimation precision with real data., 36 pages

This article investigates the predictability of dry bulk shipments' physical shipping costs while testing the predictive significance of derivative products. Accordingly, a comprehensive grid search procedure is needed to simulate combinations of model structures subject to a cross-validation process. In this regard, the intelligent model search engine (IMSE) is implemented as the search algorithm. The IMSE algorithm is selected due to its broad model space naturally consisting most of the traditional econometric model structures and advanced features, such as sample size optimization (also detects structural breaks), lag structure optimization (also reflects seasonality), among others. In contrast to the previous studies in the shipping market research, IMSE executes a relaxation of statistical significance criteria to target the out of sample predictive accuracy (instead of the goodness of fit). Sparsification is retained by utilizing a dropout procedure in line with the cross-validation process. The empirical results indicate the fact that predictive accuracy can be achieved or improved with a shorter sample period. More than half of the entire dataset has not been utilized in the final selections for most simulations. One-month and two-month maturity derivative contracts are tested for predictive features, while none of them is selected for the sparse model selections.

Machine learning research, particularly in genomics, is often based on wide shaped datasets, i.e. datasets having a large number of features, but a small number of samples. Such configurations raise the possibility of chance influence (the increase of measured accuracy due to chance correlations) on the learning process and the evaluation results. Prior research underlined the problem of generalization of models obtained based on such data. In this paper, we investigate the influence of chance on prediction and show its significant effects on wide shaped datasets. First, we empirically demonstrate how significant the influence of chance in such datasets is by showing that prediction models trained on thousands of randomly generated datasets can achieve high accuracy. This is the case even when using cross-validation. We then provide a formal analysis of chance influence and design formal chance influence estimators based on the dataset parameters, namely its sample size, the number of features, the number of classes and the class distribution. Finally, we provide an in-depth discussion of the formal analysis including applications of the findings and recommendations on chance influence mitigation.

This paper studies the optimality of matched-pair designs in randomized controlled trials (RCTs). Matched-pair designs are examples of stratified randomization, in which the researcher partitions a set of units into strata based on their observed covariates and assign a fraction of units in each stratum to treatment. A matched-pair design is such a procedure with two units per stratum. Despite the prevalence of stratified randomization in RCTs, implementations differ vastly. We provide an econometric framework in which, among all stratified randomization procedures, the optimal one in terms of the mean-squared error of the difference-in-means estimator is a matched-pair design that orders units according to a scalar function of their covariates and matches adjacent units. Our framework captures a leading motivation for stratifying in the sense that it shows that the proposed matched-pair design additionally minimizes the magnitude of the ex-post bias, i.e., the bias of the estimator conditional on realized treatment status. We then consider empirical counterparts to the optimal stratification using data from pilot experiments and provide two different procedures depending on whether the sample size of the pilot is large or small. For each procedure, we develop methods for testing the null hypothesis that the average treatment effect equals a prespecified value. Each test we provide is asymptotically exact in the sense that the limiting rejection probability under the null equals the nominal level. We run an experiment on the Amazon Mechanical Turk using one of the proposed procedures, replicating one of the treatment arms in Dellavigna and Pope (2018), and find the standard error decreases by 29%, so that only half of the sample size is required to attain the same standard error.

OBJECTIVES: Observational studies report mixed findings regarding the association between vitamin D and JIA incidence or activity, however such studies are susceptible to considerable bias. Since low vitamin D levels are common within the general population and easily corrected, there is potential public health benefit in identifying a causal association between vitamin D insufficiency and JIA incidence. To limit bias due to confounding and reverse causation we examined the causal effect of the major circulating form of vitamin D, 25-(OH)D, on JIA incidence using Mendelian randomization (MR).METHODS: In this two sample MR analysis we used summary level data from the largest and most recent genome wide association study (GWAS) of 25-(OH)D levels (sample size 443,734), alongside summary data from two JIA GWASs (sample sizes 15,872 and 12,501), all from European populations. To test and account for potential bias due to pleiotropy we employed multiple MR methods and sensitivity analyses.RESULTS: We found no evidence of a causal relationship between genetically predicted 25-(OH)D levels and JIA incidence (OR 1.00, 95% CI 0.76-1.33 per standard deviation increase in standardised natural-log transformed 25-(OH)D levels). This estimate was consistent across all methods tested. Additonally there was no evidence that genetically predicted JIA causally influences 25-(OH)D levels (-0.002 standard deviation change in standardised natural-log transformed 25-(OH)D levels per doubling odds in genetically predicted JIA, 95% CI -0.006-0.002).CONCLUSION: Given the lack of a causal relationship between 25-(OH)D levels and JIA, population level vitamin D supplementation is unlikely to reduce JIA incidence.

Introduction: Hydroxychloroquine (HCQ) has recently become the focus of attention in the current COVID-19 pandemic. With an increase in the off-label use of HCQ, concern for the safety of HCQ has been raised. We, therefore, performed this systematic review to analyze the safety data of HCQ against placebo and active treatment in various disease conditions. Methods: We searched PubMed, Embase, and Cochrane for Randomized Controlled Trials (RCTs) and Observational Studies (OSs) that evaluated HCQ for the treatment of any disease other than COVID19 in adult patients up to May 2020. We assessed the quality of the included studies using Risk of Bias 2 (for RCTs) and Newcastle–Ottawa Scale (for OSs). Data were analyzed with randomeffect meta-analysis. Sensitivity and subgroup analyses were performed to identify heterogeneity. Results: A total of 6641 studies were screened, and 49 studies (40 RCTs and 9 OSs) with a total sample size of 35044 patients were included. The use of HCQ was associated with higher risks of TDAEs as compared to placebo/no active treatment [RR 1.47, 95%CI 1.03-2.08]. When HCQ was compared with active treatments, the risks of AEs [RR 0.74, 95% CI 0.63-0.86] and TDAEs were less in the HCQ arm [RR 0.57, 95% CI 0.39-0.81]. The outcomes did not differ in the sensitivity analysis. Conclusion: The results suggest that the use of HCQ was associated with a lower risk of AEs and TDAEs as compared to active treatment, whereas posing higher risk of TDAEs as compared to placebo.

A dilemma faced by classification is that the data is not collected at the same frequency in some applications. We investigate the mixed frequency data in a new way and recognize them as a special style of multi-view data, in which each view data is collected at a different sampling frequency. This paper proposes a discriminative dictionary pair learning method constrained by ordinal locality for mixed frequency data classification (shorted by DPLOL-MF). This method integrates synthesis dictionary and analysis dictionary into a dictionary pair, which not only improves computational cost caused by the ${\ell_0}$ or ${\ell_1}$ -norm constraint, but also can deal with the sampling frequency inconsistency. The DPLOL-MF utilizes a synthesis dictionary to learn class-specified reconstruction information and employs an analysis dictionary to generate coding coefficients by analyzing samples. Particularly, the ordinal locality preserving term is leveraged to constrain the atoms of dictionaries pair to further facilitate the learned dictionary pair to be more discriminative. Besides, we design a specific classification scheme for the inconsistent sample size of mixed frequency data. This paper illustrates a novel idea to solve the classification task of mixed frequency data and the experimental results demonstrate the effectiveness of the proposed method.

Summary Introduction Prehabilitation is defined as preoperative conditioning of patients in order to improve post-operative outcomes. Some studies showed an increase in functional recovery following colorectal surgery, but its effect in hepato-pancreato-biliary (HPB) surgery is unclear. The aim of this study was to realize a systematic literature review and meta-analysis on the current available evidence on prehabilitation in HPB surgery. Materials and methods A systematic review and a metanalysis were carried out on prehabilitation (physical, nutritional and psychological interventions) in HPB surgery (2009-2019). Assessed outcomes were postoperative complications, length of stay (LOS), 30-day readmission, and mortality. Main results Four studies among the 191 screened were included in this systematic review (3 randomized controlled trials, 1 case-control propensity score study), involving 419 patients (prehabilitation group, n = 139; control group, n = 280). After pooling, no difference was observed on LOS ((−4.37 days [95% CI: −8.86; 0.13]) or postoperative complications (RR 0.83 [95%CI: 0.62; 1.10]), reported by all the included studies. Two trials reported on readmission rate, but given the high heterogeneity, a meta-analysis was not realized. No deaths were reported among the included studies. Conclusion No effect of prehabilitation programs in HPB surgery was observed on LOS or postoperative complications rate. Future trials with standardized outcomes of measure, and adequately powered samples calculations are thus required. PROSPERO registration CRD42020165218.

This paper develops generalized method of moments (GMM) estimation and inference procedures for quantile regression models. We propose a GMM estimator for simultaneous estimation across multiple quantiles. This estimator allows us to model quantile regression coefficients using flexible parametric restrictions across quantiles. The restrictions and simultaneous estimation lead to efficiency gains compared to standard methods. We establish the asymptotic properties of the GMM estimators when the number of quantiles used is fixed and when it diverges to infinity jointly with the sample size. As an alternative to GMM, we also propose a minimum distance estimator over a given subset of quantiles. Moreover, we provide specification tests for the imposed restrictions. The estimators and tests we propose are simple to implement in practice. Monte Carlo simulations provide numerical evidence of the finite sample properties of the methods. Finally, we apply the proposed methods to estimate the effects of smoking on birthweight of live infants at the extreme bottom of the conditional distribution.

A number of powerful demographic inference methods have been developed in recent years, with the goal of fitting rich evolutionary models to genetic data obtained from many populations. In this paper we investigate the statistical performance of these methods in the specific case where there is continuous migration between populations. Compared with earlier work, migration significantly complicates the theoretical analysis and requires new techniques. We employ the theories of phase-type distributions and concentration of measure in order to study the two-island and isolation-with-migration models, resulting in both upper and lower bounds on rates of convergence for parametric estimators in migration models. For the upper bounds, we consider inferring rates of coalescent and migration on the basis of directly observing pairwise coalescent times, and, more realistically, when (conditionally) Poisson-distributed mutations dropped on latent trees are observed. We complement these upper bounds with information-theoretic lower bounds which establish a limit, in terms of sample size, below which inference is effectively impossible.

How variable is the functionally-defined structure of early visual areas in human cortex and how much variability is shared between twins? Here we quantify individual differences in the best understood functionally-defined regions of cortex: V1, V2, V3. The Human Connectome Project 7T Retinotopy Dataset includes retinotopic measurements from 181 subjects, including many twins. We trained four “anatomists” to manually define V1-V3 using retinotopic features. These definitions were more accurate than automated anatomical templates and showed that surface areas for these maps varied more than three-fold across individuals. This three-fold variation was little changed when normalizing visual area size by the surface area of the entire cerebral cortex. In addition to varying in size, we find that visual areas vary in how they sample the visual field. Specifically, the cortical magnification function differed substantially among individuals, with the relative amount of cortex devoted to central vision varying by more than a factor of 2. To complement the variability analysis, we examined the similarity of visual area size and structure across twins. Whereas the twin sample sizes are too small to make precise heritability estimates (50 monozygotic pairs, 34 dizygotic pairs), they nonetheless reveal high correlations, consistent with strong effects of the combination of shared genes and environment on visual area size. Collectively, these results provide the most comprehensive account of individual variability in visual area structure to date, and provide a robust population benchmark against which new individuals and developmental and clinical populations can be compared.Significance StatementAreas V1, V2, and V3 are among the best studied functionally-defined regions in human cortex. Using the largest retinotopy dataset to date, we characterized the variability of these regions across individuals and the similarity between twin pairs. We find that the size of visual areas varies dramatically (up to 3.5x) across healthy young adults, far more than the variability of the cerebral cortex size as a whole. Much of this variability appears to arise from inherited factors, as we find very high correlations in visual area size between monozygotic twin-pairs, and lower but still substantial correlations between dizygotic twin pairs. These results provide the most comprehensive assessment of how functionally defined visual cortex varies across the population to date.

Background Three-dimensional (3D) models improve the comprehension of renal anatomy. Objective To evaluate the impact of novel 3D-derived parameters, to predict surgical outcomes after robot-assisted partial nephrectomy (RAPN). Design, setting, and participants Sixty-nine patients with cT1-T2 renal mass scheduled for RAPN were included. Three-dimensional virtual modeling was achieved from computed tomography. The following volumetric and morphological 3D parameters were calculated: VT (volume of the tumor); VT/VK (ratio between tumor volume and kidney volume); CSA3D (ie, contact surface area); UCS3D (contact to the urinary collecting system); Tumor-Artery3D: tumor’s blood supply by tertiary segmental arteries (score = 1), secondary segmental artery (score = 2), or primary segmental/main renal artery (scoren = 3); ST (tumor’s sphericity); ConvT (tumor’s convexity); and Endophyticity3D (ratio between the CSA3D and the global tumor surface). Intervention RAPN with a 3D model. Outcome measurements and statistical analysis Three-dimensional parameters were compared between patients with and without complications. Univariate logistic regression was used to predict overall complications and type of clamping; linear regression was used to predict operative time, warm ischemia time, and estimated blood loss. Results and limitations Overall, 11 (15%) individuals experienced overall complications (7.2% had Clavien ≥3 complications). Patients with urinary collecting system (UCS) involvement at 3D model (UCS3D = 2), tumor with blood supply by primary or secondary segmentary arteries (Tumor-Artery3D = 1 and 2), and high Endophyticity3D values had significantly higher rates of overall complications (all p ≤ 0.03). At univariate analysis, UCS3D, Tumor-Artery3D, and Endophyticity3D are significantly associated with overall complications; CSA3D and Endophyticity3D were associated with warm ischemia time; and CSA3D was associated with selective clamping (all p ≤ 0.03). Sample size and the lack of interobserver variability are the main limits. Conclusions Three-dimensional modeling provides novel volumetric and morphological parameters to predict surgical outcomes after RAPN. Patient summary Novel morphological and volumetric parameters can be derived from a three-dimensional model to describe surgical complexity of renal mass and to predict surgical outcomes after robot-assisted partial nephrectomy.

During the COVID-19 pandemic, there has been an increasing rollout of non-contact fever screening solutions to assist in curbing the spread of disease. This study begins by describing how screening for disease has historically been performed. It proposes four measurement characteristics of an ideal screening solution: non-contact, effective, rapid and low-cost measurements. Next, it reviews the existing literature on fever-screening using non-contact infrared thermometer (NCIT) devices as well as infrared thermography (IRT) devices, as these are two technologies which have experienced increasing use. For this review, 185 research papers were identified, 21 research studies were included after inclusion and exclusion criteria were applied. A total of 35 experiments were identified for analysis and their results tabulated. Of these studies, 66% are IRT and 34% are NCIT, with a median sample size of 430 subjects. 26 experiments involve febrile participants, with a median febrile percentage of 11.22 % of population. The reported sensitivity of febrile detection using NCIT varies from 3.7% to 97% and when using IRT it varies from 15% to 100%. Both indoor and outdoor studies are investigated, as well as those conducted in acute and non-acute settings. The results of this review show a clear lack of consensus on the effectiveness of these systems. Overall, these results indicate that sensitivity and specificity are reduced when using IRT and NCIT technologies compared to other thermometers used in medical practice. Their use should be carefully assessed based on the risks present in each particular measurement scenario. CCBY

We study the finite sample behavior of Lasso-based inference methods such as post double Lasso and debiased Lasso. We show that these methods can exhibit substantial omitted variable biases (OVBs) due to Lasso not selecting relevant controls. This phenomenon can occur even when the coefficients are sparse and the sample size is large and larger than the number of controls. Therefore, relying on the existing asymptotic inference theory can be problematic in empirical applications. We compare the Lasso-based inference methods to modern high-dimensional OLS-based methods and provide practical guidance., Comment: Final author version, accepted at The Review of Economics and Statistics

Context Athletes with anterior cruciate ligament (ACL) reconstruction (ACLR) exhibit increased cortical motor planning during simple sensorimotor tasks compared with healthy athletes serving as control groups. This may interfere with proper decision making during time-constrained movements, elevating the reinjury risk. Objective To compare cortical motor planning and biomechanical stability during jump landings between participants with ACLR and healthy individuals. Design Cross-sectional study. Setting Laboratory. Patients or Other Participants Ten men with ACLR (age = 28 ± 4 years, time after surgery = 63 ± 35 months) and 17 healthy men (age = 28 ± 4 years) completed 43 ± 4 preplanned (landing leg shown before takeoff) and 51 ± 5 unplanned (visual cue during flight) countermovement jumps with single-legged landings. Main Outcome Measure(s) Movement-related cortical potentials (MRCPs) and frontal θ frequency power before the jump were analyzed using electroencephalography. Movement-related cortical potentials were subdivided into 3 successive 0.5-second time periods (readiness potential [RP]-1, RP-2, and negative slope [NS]) relative to movement onset, with higher values indicating more motor planning. Theta power was calculated for the last 0.5 second before movement onset, with higher values demonstrating more focused attention. Biomechanical landing stability was measured via peak vertical ground reaction force, time to stabilization, and center of pressure. Results Both the ACLR and healthy groups evoked MRCPs at all 3 time periods. During the unplanned task analyzed using P values and Cohen d, the ACLR group exhibited slightly higher but not different MRCPs, achieving medium effect sizes (RP-1: P = .25, d = 0.44; RP-2: P = .20, d = 0.53; NS: P = .28, d = 0.47). The ACLR group also showed slightly higher θ power values that were not different during the preplanned (P = .18, d = 0.29) or unplanned (P = .42, d = 0.07) condition, achieving small effect sizes. The groups did not differ in their biomechanical outcomes (P values > .05). No condition × group interactions occurred (P values > .05). Conclusions Our jump-landing task evoked MRCPs. Although not different between groups, the observed effect sizes provided the first indication that men with ACLR might have consistently relied on more cortical motor planning associated with unplanned jump landings. Confirmatory studies with larger sample sizes are warranted.

We study the rapidly growing literature on the causal effects of financial education programs in a meta-analysis of 76 randomized experiments with a total sample size of over 160,000 individuals. Many of these experiments are published in top economics and finance journals. The evidence shows that financial education programs have, on average, positive causal treatment effects on financial knowledge and downstream financial behaviors. Treatment effects are economically meaningful in size, similar to those realized by educational interventions in other domains, and robust to accounting for publication bias in the literature. We also discuss the cost-effectiveness of financial education interventions.

High-dimensional linear regression has been intensively studied in the community of statistics in the last two decades. For the convenience of theoretical analyses, classical methods usually assume independent observations and sub-Gaussian-tailed errors. However, neither of them hold in many real high-dimensional time-series data. Recently [Sun, Zhou, Fan, 2019, J. Amer. Stat. Assoc., in press] proposed Adaptive Huber Regression (AHR) to address the issue of heavy-tailed errors. They discover that the robustification parameter of the Huber loss should adapt to the sample size, the dimensionality, and the moments of the heavy-tailed errors. We progress in a vertical direction and justify AHR on dependent observations. Specifically, we consider an important dependence structure -- Markov dependence. Our results show that the Markov dependence impacts on the adaption of the robustification parameter and the estimation of regression coefficients in the way that the sample size should be discounted by a factor depending on the spectral gap of the underlying Markov chain.

BACKGROUND Donepezil had been recognized to have impact on sleep quality in demented patients. However, there was insufficient evidences about the actual effect of donepezil in the sleep architectures. Our meta-analysis aimed to evaluate the changes of sleep architectures related to donepezil use. METHODS Followed the PRISMA2020 and AMSTAR2 guidelines, electronic search had been performed on the databases of PubMed, Embase, ScienceDirect, ClinicalKey, Cochrane CENTRAL, ProQuest, Web of Science, and ClinicalTrials.gov. The outcome measurement was changes of sleep parameters detected by polysomnography. A random-effects meta-analysis was conducted. RESULTS Total twelve studies had been involved. The percentage of REM sleep would significantly increase after donepezil treatment (Hedges' g = 0.694, p

Background: The use of levetiracetam (LEV) has been increasing given its favorable pharmacokinetic profile. Numerous population pharmacokinetic studies for LEV have been conducted. However, there are some discrepancies regarding factors affecting its pharmacokinetic variability. Therefore, this systematic review aimed to summarize significant predictors for LEV pharmacokinetics as well as the need for dosage adjustments. Methods: We performed a systematic search for population pharmacokinetic studies of LEV conducted using a nonlinear-mixed effect approach from PubMed, Scopus, CINAHL Complete, and Science Direct databases from their inception to March 2020. Information on study design, model methodologies, significant covariate-parameter relationships, and model evaluation was extracted. The quality of the reported studies was also assessed. Results: A total of 16 studies were included in this review. Only two studies were conducted with a two-compartment model, while the rest were performed with a one-compartment structure. Bodyweight and creatinine clearance were the two most frequently identified covariates on LEV clearance (CLLEV). Additionally, postmenstrual age (PMA) or postnatal age (PNA) were significant predictors for CLLEV in neonates. Only three studies externally validated the models. Two studies conducted pharmacodynamic models for LEV with relatively small sample size. Conclusion: Significant predictors for LEV pharmacokinetics are highlighted in this review. For future research, a population pharmacokinetic-pharmacodynamic model using a larger sample size should be conducted. From a clinical perspective, the published models should be externally evaluated before clinical implementation.

Chronic Kidney Disease patients undergoing hemodialysis can cause stress to the patient. The longer the patient suffers from Chronic Kidney Disease, the more the patient has a variety of experiences with stressors due to the disease. These experiences can be used as an anticipatory effort in dealing with stressors experienced by patients. The purpose of this was the relationship between the duration of hemodialysis and the coping mechanism of patients with chronic kidney disease undergoing hemodialysis. This study used descriptive quantitative, with a correlation design and a cross-sectional approach. The sample was 111 patients with chronic kidney disease undergoing hemodialysis. Sampling technique used the purposive sampling technique. The data collection instrument used a jalowiec coping scale questionnaire and length of underwent hemodialysis. The results showed that most respondents underwent hemodialysis in the category

Recommender systems personalize content by recommending items to users. Item recommendation algorithms are evaluated by metrics that compare the positions of truly relevant items among the recommended items. To speed up the computation of metrics, recent work often uses sampled metrics where only a smaller set of random items and the relevant items are ranked. This paper investigates such sampled metrics in more detail and shows that they are inconsistent with their exact counterpart, in the sense that they do not persist relative statements, for example, recommender A is better than B , not even in expectation. Moreover, the smaller the sample size, the less difference there is between metrics, and for very small sample size, all metrics collapse to the AUC metric. We show that it is possible to improve the quality of the sampled metrics by applying a correction, obtained by minimizing different criteria. We conclude with an empirical evaluation of the naive sampled metrics and their corrected variants. To summarize, our work suggests that sampling should be avoided for metric calculation, however if an experimental study needs to sample, the proposed corrections can improve the quality of the estimate.

Voting rules can be assessed from quite different perspectives: the axiomatic, the pragmatic, in terms of computational or conceptual simplicity, susceptibility to manipulation, and many others aspects. In this paper, we take the machine learning perspective and ask how prominent voting rules compare in terms of their learnability by a neural network. To address this question, we train the neural network to choosing Condorcet, Borda, and plurality winners, respectively. Remarkably, our statistical results show that, when trained on a limited (but still reasonably large) sample, the neural network mimics most closely the Borda rule, no matter on which rule it was previously trained. The main overall conclusion is that the necessary training sample size for a neural network varies significantly with the voting rule, and we rank a number of popular voting rules in terms of the sample size required.

Introduction and objectives Most of the studies evaluating the effect of cross links on spinal stability are performed in vitro on porcine or human spine segments and there is limited data regarding clinical benefits of cross link augmentation in traumatic injuries. In this study we aimed to evaluate the effects of cross-links insertion between rods on the fusion rates and post-surgical patients’ satisfaction among patients with traumatic thoracolumbar fractures who underwent posterior spinal fixation with pedicle screws. Materials and methods This study was conducted as a randomized clinical trial on 60 patients suffering from traumatic thoracolumbar vertebrae fractures. Patients were randomized into three groups: A (without any cross-link), B (One cross-link insertion) and C (two cross-links insertion). Six months after surgery outcomes were evaluated: fusion rates (plain X-ray and CT scan), Back pain (Visual Analog Scale) and patient satisfaction (fair, good, excellent). Results In group A 13 (65%) patients had structured bone fusion, but in 7 (35%) patients bone fusion was not observed. In both groups B and C, 19 patients (95%) had bone fusion, but only in 1 patient (5%) fusion failed (p = 0.009). In group A, fair satisfaction has the highest rate (8 patients (40%)) compared to the other groups. The highest reported severity of back pain was observed in group A while the lowest reported intensity of back pain was related to group B (p = 0.001). Conclusions Adding cross link to posterior spinal fixations of patients with traumatic thoracolumbar fractures can be associated with better final fusion results and patients’ satisfaction. However it is necessary to design studies with greater sample sizes to confirm this theory. Trial registration number: IRCT20120527009878N3.

The status of malnutrition and malnutrition is caused by various interrelated factors, including family behavior factors in providing food, breastfeeding and complementary feeding, and nutrition service efforts. This study aims to analyze the relationship between nutritionally conscious families (Kadarzi) and the nutritional status of children under five in the Work Area of the Puskesmas Pelambuan, Banjarmasin. This study used an analytic method with a cross sectional approach. The sampling technique used was proportional random sampling with a sample size of 82 parents of children under five. The measuring instrument used was a questionnaire and observation with weighing tools, the data analysis used the Spearman rank test. The location of this research was conducted in the Working Area of the Pelambuan Health Center, Banjarmasin City. Based on the results of the study, families who were aware of nutrition tended to have normal nutritional status totaling 27 (79.4%) and more than 7 (20.6%). Meanwhile, families who were not aware of nutrition tended to have malnutrition status, amounting to 31 people (64.6%) and malnutrition status 17 (35.4%). The spearman rank statistical test obtained p value 0.000 smaller than α = 0.05. There is a relationship between nutritionally conscious families (adarzi) with the nutritional status of children under five in the working area of Puskesmas Pelambuan, Banjarmasin City. Keywords : Nutritional awareness family, nutritional status, children

Objective There is an unmet need for therapies that target the underlying pathophysiology of osteoarthritis (OA). However, defining appropriate measures for clinical trials of such therapies is challenging. Our objective is to propose concept clinical endpoints that directly capture clinical benefit in this setting and evaluate the feasibility of their use. Methods This analysis used the multi-center, longitudinal, observational Osteoarthritis Initiative (OAI) database. OAI participants primarily had knee OA, with follow-up of up to nine years and assessments of joints, surgical interventions, performance outcomes, and patient-reported outcomes (PROs). We examined this dataset to identify existing outcome measures of direct clinical benefit. We evaluated the feasibility of conducting trials using these candidate endpoints by estimating incidence rates and resulting required sample sizes and study durations in time-to-event analyses. Results We identified candidate endpoints based on total knee replacement (TKR) and composite endpoints defined by TKR and conservative thresholds of PROs of pain and function. Using time to TKR as an endpoint, a study with an average follow-up time of three years requires approximately 3,000 to 18,000 subjects depending on effect size. Alternatively, a composite endpoint such as 'time to TKR or severe pain or severely impaired functioning', the required sample sizes ranged from approximately 2,000 to 11,000 for a three-year study. Conclusion The proposed concept endpoints can reliably and feasibly evaluate effectiveness of therapies for this unmet need. In particular, the composite endpoint approach can substantially reduce sample sizes (up to approximately 40%) compared to the use of TKR alone.

Background: This study aimed at understanding the trend in extracellular fluid (ECF) change and estimating the efficacy of the early complex decongestive therapy (CDT) program in terms of prevalence of lower limb lymphedema, as well as at identifying the quality of life (QoL) associated with response to CDT after gynecological cancer therapy. Methods and Results: Thirty-one patients undergoing gynecological cancer treatment were enrolled and randomly assigned to the CDT and control groups. In the CDT group, CDT was started within 2 weeks after surgery for 30 minutes a day, for 2 weeks (5 days per week). The patients' QoL was assessed through the European Organization for Research and Treatment of Cancer Quality-of-Life Questionnaire; version 3. Inbody S10® (Biospace, Seoul, South Korea) was used to calculate the ECF. The assessment was performed at pre- and post-surgery and at 3, 6, and 12 months after surgery. Statistically significant differences, in favor of the CDT group, were encountered at the 3-month follow-up visit, especially with respect to the QoL score. Regarding the prevalence of lymphedema, no significant difference was observed between groups. The mean value of the impedance ratio was low in the CDT group during the entire 12 month follow-up. Conclusions: The results of the present study show that early CDT had a positive effect on the prevalence of lymphedema and body composition. Long-term follow-up studies with a larger sample size are needed to warrant the time-group effect of early rehabilitation.

BACKGROUND/SIGNIFICANCE The pain-reducing effect of applying vibration, which is used as a nonpharmacological method in pain management, is explained by the gate control theory developed by Melzack and Wall. Studies that are based on this theory have shown similar results to those of Melzack and Wall, indicating that pain-transmitting nerves are suppressed by vibrations, leading to higher pain thresholds. PURPOSE The study aimed to evaluate the effect of applying vibration to manage pain during heel lance procedures in newborns. METHODS This is a randomized, controlled experimental study. The study sample included 56 newborns determined using power analysis (vibration group = 28; control group = 28). For those in the vibration group, a vibrating device was used for approximately 30 seconds before the heel lance procedure and then continued throughout the procedure. No interventions were provided to the newborns in the control group. Pain in the newborns was evaluated by the nurse who performed the heel lance procedures, before the procedures and 15 to 20 seconds and 5 minutes after procedures, and by 2 specialists who viewed the video footage and conducted the Neonatal Infant Pain Scale examination. RESULTS The pain scores were significantly lower at 15 to 20 seconds and 5 minutes after the heel lance procedures in the group that received vibrations than in the control group (P < .05). IMPLICATIONS FOR PRACTICE The application of vibration, which is effective, nonpharmacological, and noninvasive, could help nurses manage pain in newborns as a nonpharmacological method. IMPLICATIONS FOR RESEARCH The number of studies that examine the effect that vibration has on pain associated with heel lance procedures in newborns is very limited, and it is recommended that further studies be conducted with larger sample sizes.

Conditional value at risk (CVaR) is a popular measure for quantifying portfolio risk. Sensitivity analysis of CVaR is common in risk management and gradient-based optimization algorithms. In this paper, we study the infinitesimal perturbation analysis estimator for CVaR sensitivity using randomized quasi-Monte Carlo (RQMC) simulation. RQMC has proved valuable in financial option pricing with a better rate of convergence compared to Monte Carlo sampling, but theoretical guarantees for this new application of RQMC shall be studied. To this end, we first prove that the RQMC-based estimator is strongly consistent under very mild conditions. Under some technical conditions, RQMC yields a mean error rate of O ( n − 1 / 2 − 1 / ( 4 d − 2 ) + ϵ ) for arbitrarily small ϵ > 0 , where d represents the dimension of RQMC points and n is the sample size. Some typical applications of CVaR sensitivity estimation are conducted to both show how the theoretical results can be applied, as well as to provide numerical results documenting the superiority of the RQMC estimator.

The accuracy of smartphone-based positioning systems using WiFi usually suffers from ranging errors caused by non-line-of-sight (NLOS) conditions. Previous research usually exploits several distribution features from a long time series (hundreds of samples) of WiFi received signal strength (RSS) or WiFi round-trip time (RTT) to achieve a high identification accuracy. However, the long time series or large sample size attributes to high power and time consumption in data collection for both training and testing. This will also undoubtedly be detrimental to user experience as the waiting time for getting enough samples is quite long. Therefore, this paper proposes three new real-time NLOS/LOS identification methods for smartphone-based indoor positioning systems using WiFi RSS and RTT distance measurement (RDM). Based on our extensive analysis of RSS and RDM dispersion features, three machine learning algorithms were chosen and developed to separate the samples for NLOS/LOS conditions. Experiments show that our best method achieves a discrimination accuracy of over 96% with a sample size of 10. Considering the theoretically shortest WiFi ranging interval of 100ms of the RTT-enabled smartphones, our algorithm is able to provide the shortest latency of 1s to get the testing result among all of the state-of-art methods.

The key statistical properties of the Root Mean Square Error (RMSE) and the Mean Absolute Error (MAE) estimators were derived in this study for zero mean symmetric error distributions. A density function, named the Approximate Root Normal Distribution (ARND), was developed to approximate the distribution of a square root of a normal random variable. This enabled approximating the distribution of the RMSE estimator. The theoretical derivations and the demonstrations on common distributions, a benchmark time series, and a real world data set (with prediction errors generated from ANN and ARIMA models) lead to the following practically useful findings. When comparing errors having the same distribution type, RMSE was shown to be preferred for platykurtic distributions, MAE for leptokurtic distributions, and either RMSE or MAE for mesokurtic distributions. For different distribution types, however, using the two estimators alone was shown to lead to erroneous conclusions. The revelation that the estimated RMSE/MSE ratio could identify whether the errors came from platykurtic/mesokurtic/leptokurtic distributions was a useful complementary result. Comparison of errors based on, error distributions, sample size and the standard errors of the estimators, was discussed. The proposed procedure for deriving the statistical properties of the two estimators has scope for extension for other distribution types.

Pedestrians' red-light crossing can present a threat to traffic safety. Among all the existing work related to pedestrian's red-light crossing, there are few studies using trajectory data in time sequence. This paper uses pose estimation (keypoint detection) to generate pedestrians' variables from CCTV videos. Four machine learning models are used to predict pedestrians' crossing intention at intersections' red-light. The best model achieves an accuracy of 0.920 and AUC value of 0.849, with data from three intersections. Different prediction horizons (up to 4 sec) are used. With longer prediction horizons, the sample size gets smaller, which partially leads to worse model performance. However, the performance with prediction horizon up to 2 sec is still good (AUC value as 0.841). It is found that keypoint variables such as the angles between ankle and knee (left side) and elbow and shoulder (right side) are important. This model can be further implemented in the Infrastructure-to-Vehicle (I2V) applications and thus prevent accidents due to pedestrians' red-light crossing by issuing warnings to drivers.

Physical activity (PA) and exercise have numerous benefits in Cystic Fibrosis (CF) including improved lung function, exercise capacity and quality of life. Despite these benefits, the effectiveness of interventions to promote PA in this population are still largely unknown. The objective of this review was to synthesise existing research and determine whether exercise interventions are effective in promoting PA in people with CF. Using the PRISMA guidelines, a comprehensive search was conducted. Fifteen studies (463 participants) met the inclusion criteria. Eleven studies demonstrated improvements in PA in both short- and long-term interventions. However, the interventions were variable across the included studies, with a large inconsistency in PA assessment tools used. Aerobic training and activity counselling were the two elements identified in this review which most consistently improved PA. Future research should consider larger sample sizes and the use of accurate instruments to assess and track PA levels longitudinally.

Objectives To investigate the general population’s view on artificial intelligence (AI) in medicine with specific emphasis on 3 areas that have experienced major progress in AI research in the past few years, namely radiology, robotic surgery, and dermatology. Methods For this prospective study, the April 2020 Online Longitudinal Internet Studies for the Social Sciences Panel Wave was used. Of the 3117 Longitudinal Internet Studies For The Social Sciences panel members contacted, 2411 completed the full questionnaire (77.4% response rate), after combining data from earlier waves, the final sample size was 1909. A total of 3 scales focusing on trust in the implementation of AI in radiology, robotic surgery, and dermatology were used. Repeated-measures analysis of variance and multivariate analysis of variance was used for comparison. Results The overall means show that respondents have slightly more trust in AI in dermatology than in radiology and surgery. The means show that higher educated males, employed or student, of Western background, and those not admitted to a hospital in the past 12 months have more trust in AI. The trust in AI in radiology, robotic surgery, and dermatology is positively associated with belief in the efficiency of AI and these specific domains were negatively associated with distrust and accountability in AI in general. Conclusions The general population is more distrustful of AI in medicine unlike the overall optimistic views posed in the media. The level of trust is dependent on what medical area is subject to scrutiny. Certain demographic characteristics and individuals with a generally positive view on AI and its efficiency are significantly associated with higher levels of trust in AI.

Background Misclassification bias is introduced into medical claims-based research because of reliance on diagnostic coding rather than full medical record review. We sought to characterize this bias for idiopathic intracranial hypertension (IIH) and evaluate strategies to reduce it. Methods A retrospective review of medical records was conducted using a clinical data warehouse containing medical records and administrative data from an academic medical center. Patients with 1 or more instances of International Classification of Diseases (ICD)-9 or -10 codes for IIH (348.2 or G93.2) between 1989 and 2017 and original results of neuroimaging (head CT or MRI), lumbar puncture, and optic nerve examination were included in the study. Diagnosis of IIH was classified as definite, probable, possible, or inaccurate based on review of medical records. The positive predictive value (PPV) for IIH ICD codes was calculated for all subjects, subjects with an IIH code after all testing was completed, subjects with high numbers of IIH ICD codes and codes spanning longer periods, subjects with IIH ICD codes associated with expert encounters (ophthalmology, neurology, or neurosurgery), and subjects with acetazolamide treatment. Results Of 1,005 patients with ICD codes for IIH, 103 patients had complete testing results and were included in the study. PPV of ICD-9/-10 codes for IIH was 0.63. PPV in restricted samples was 0.82 (code by an ophthalmologist n = 57), 0.70 (acetazolamide treatment n = 87), and 0.72 (code after all testing, n = 78). High numbers of code instances and longer duration between the first and last code instance also increased the PPV. Conclusions An ICD-9 or -10 code for IIH had a PPV of 63% for probable or definite IIH in patients with necessary diagnostic testing performed at a single institution. Coding accuracy was improved in patients with an IIH ICD code assigned by an ophthalmologist. Use of coding algorithms considering treatment providers, number of codes, and treatment is a potential strategy to reduce misclassification bias in medical claims-based research on IIH. However, these are associated with a reduced sample size.

Assisting end users to identify desired results from a large dataset is an important problem for multi-criteria decision making. To address this problem, top-k and skyline queries have been widely adopted, but they both have inherent drawbacks, i.e., the user either has to provide a specific utility function or faces many results. The k-regret minimization query is proposed, which integrates the merits of top-k and skyline queries. Due to the NP-hardness of the problem, the k-regret minimization query is time consuming and the greedy framework is widely adopted. However, formal theoretical analysis of the greedy approaches for the quality of the returned results is still lacking. In this paper, we first fill this gap by conducting a nontrivial theoretical analysis of the approximation ratio of the returned results. To speed up query processing, a sampling-based method, S toc P res G reed , is developed to reduce the evaluation cost. In addition, a theoretical analysis of the required sample size is conducted to bound the quality of the returned results. Finally, comprehensive experiments are conducted on both real and synthetic datasets to demonstrate the efficiency and effectiveness of the proposed methods.

Atrial fibrillation after cardiac surgery (AFACS) is a serious postoperative complication. There is significant research interest in this field but also relevant heterogeneity in reported AFACS definitions and approaches used for its identification. Few data exist on the extent of this variation in clinical studies. The authors reviewed the literature since 2001 and included manuscripts reporting outcomes of AFACS in adults. They excluded smaller studies and studies in which patients did not undergo a sternotomy. The documented protocol in each manuscript was analyzed according to six different categories to determine how AFACS was defined, which techniques were used to identify it, and the inclusion and/or exclusion criteria. They also noted when a category was not described in the documented protocol. The authors identified 302 studies, of which 92 were included. Sixty-two percent of studies were randomized controlled trials. There was significant heterogeneity in the manuscripts, including the exclusion of patients with preoperative AF, the definition and duration of AF needed to meet the primary endpoint, the type of screening approach (continuous, episodic, or opportunistic), the duration of monitoring during the study period in days, the diagnosis with predefined electrocardiogram criteria, and the requirement for independent confirmation by study investigators. Furthermore, the definitions of these criteria frequently were not described. Consistent reporting standards for AFACS research are needed to advance scientific progress in the field. The authors here propose pragmatic standards for trial design and reporting standards. These include adequate sample size estimation, a clear definition of the AFACS endpoints, and a protocol for AFACS detection.

We propose a locality preserving partial least squares discriminant analysis (LPPLSDA) which adds a locality preserving feature to the conventional partial least squares discriminant analysis(PLS-DA). The locality preserving feature captures the within group structural information via a similarity graph. The ability of LPPLS-DA to capture local structures allows it to be better suited for face recognition. We evaluate the performance of our proposed method on several benchmarked face databases which offer different levels of complexity in terms of sample size as well as image acquisition conditions. The experimental results indicate that, for each database used, the proposed method consistently outperformed the conventional PLS-DA method.

Accurate diagnosis of a disease is essential in healthcare. Prediction models, based on classical regression techniques, are widely used in clinical practice. Machine Learning (ML) techniques might be preferred in case of a large amount of data per patient and relatively limited numbers of subjects. However, this increases the risk of overfitting, and external validation is imperative. However, in the field of ML, new and more efficient techniques are developed rapidly, and if recruiting patients for a validation study is time consuming, the ML technique used to develop the first model might have been surpassed by more efficient ML techniques, rendering this original model no longer relevant. We demonstrate a stepwise design for simultaneous development and validation of prediction models based on ML techniques. The design enables - in one study - evaluation of the stability and robustness of a prediction model over increasing sample size as well as assessment of the stability of sensitivity/specificity at a chosen cut-off. This will shorten the time to introduction of a new test in health care. We finally describe how to use regular clinical parameters in conjunction with ML based predictions, to further enhance differentiation between subjects with and without a disease.

Tradeoffs occur when deciding between improving forest inventory precision by increasing sample size or by augmenting cluster plot design factors like size or subplot separation distance. The nature of these tradeoffs changes with variation in type and scale of the spatial pattern of the attribute of interest. To understand the impacts of relationships between type and scale of spatial heterogeneity and cluster plot design efficiency, we constructed a factorial simulation experiment and analysed relationships among forest inventory cost, cluster plot design factors, and different spatial heterogeneity scenarios constructed via simulation. To calculate cost, we constructed a cost model that accounted for both on- and between-plot costs. We found that type and scale of heterogeneity have important implications for plot design choices. Homogeneous stands and landscapes are the least costly to inventory. Subplot area and count have stronger impacts than subplot separation on cost efficiency, particularly in landscapes with aggregated forest patterns and in stands with homogeneous tree patterns. We discuss results in the context of the physical interaction between cluster plot geometry and spatial patterns at different scales, provide computer code for simulations, and suggest principles that forest inventory cluster plot design specialists should consider when designing inventories.

PURPOSE To compare Patient-Reported Outcomes Measurement Information System (PROMIS) Physical Function (PF) with traditional ("legacy") patient-reported outcome measures (PROMs) in regard to correlations, ease of use, and quality criteria for upper (UE) and lower extremity (LE) orthopaedic conditions. METHODS A systematic search of the PubMed/MEDLINE database was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify published articles that referenced the various PROMIS PF measures. Two authors independently reviewed selected studies. The search returned 857 studies, 85 of which were selected for independent review by 2 authors. Of these, 54 were selected for inclusion. Mixed linear models were performed to assess for differences between legacy PROMs and PROMIS measures. RESULTS The combined sample size of all included studies yielded 6,074 UE and 9,366 LE patients. Overall, PROMIS PF measures demonstrated strong correlations with legacy PROMs among UE (weighted Pearson correlation, 0.624, standard error [SE] = 0.042; weighted Spearman correlation, 0.566, SE = 0.042) and LE patients (weighted Pearson correlation, 0.645, SE = 0.062; weighted Spearman correlation, 0.631, SE = 0.041). PROMIS PF questionnaires completed by UE patients had fewer questions than legacy PROMs (5.9 vs 17.7, P = .0093) and were completed in less time (90.5 vs 223.8 seconds, P = .084). PROMIS PF questionnaires completed by LE patients had fewer questions than legacy PROMs (4.81 vs 15.33, P < .001) and were completed in less time (63.6 vs 203.2 seconds, P = .0063). The differences for the reliability measures were not significant. CONCLUSIONS PROMIS PF scores correlate strongly with commonly used legacy PROMs in orthopaedics, particularly in UE and LE patients. PROMIS PF forms can be administered efficiently and to a broad patient population while remaining highly reliable. Therefore, they can be justified for standardized use among orthopaedic patients with UE and LE conditions, improving the ability to aggregate and compare outcomes in orthopaedic research. LEVEL OF EVIDENCE Level IV, systematic review of Level I-IV evidence.