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OBJECTIVE American Indian/Alaska Native (AI/AN) people have historically been excluded from eating disorder research. Consequently, not much is known about the validity of eating disorder assessment measures in this group. The purpose of the current study was to examine the factor structure of a short measure for eating pathology, the seven-item Eating Disorder Examination Questionnaire (EDE-Q7), in AI/AN undergraduate women. Exploratory analyses examined the measurement invariance of the EDE-Q7 across AI/AN, White, and Latinx undergraduates. METHODS A sample of 150 AI/AN undergraduate women completed the full EDE-Q (28 items) as part of two larger studies. The seven items comprising the EDE-Q7 were selected from the full measure to test the factor structure. Random samples of White and Latinx women of equal sample size were chosen from the larger studies to test measurement invariance. RESULTS Confirmatory factor analyses found support for the EDE-Q7 factor structure in AI/AN undergraduate women, and scores were strongly positively correlated with the original EDE-Q global scale and disordered eating behaviors. Exploratory analyses found support for the measurement invariance of the EDE-Q7 across AI/AN, White, and Latinx women. One-way ANOVAs showed no significant racial and ethnic differences on the EDE-Q7. DISCUSSION The current study found psychometric support for the EDE-Q7 in AI/AN undergraduate women and provided preliminary evidence that the EDE-Q7 can be meaningfully compared across AI/AN, White, and Latinx undergraduate women. Further research should continue to investigate the EDE-Q7 and other eating disorder measures in AI/AN and other historically excluded groups. LEVEL OF EVIDENCE V, cross-sectional descriptive study.

We report the results of a novel protocol for running online experiments using a combination of an online experimental platform in parallel with web-conferencing software in two formats—with and without subject webcams—to improve subjects’ attention and engagement. We compare the results between our online sessions with the offline (lab) sessions of the same experiment. We find that both online formats lead to comparable subject characteristics and performance as the offline (lab) experiment. However, the webcam-on protocol has less noisy data, and hence better statistical power, than the protocol without a webcam. The webcam-on protocol can detect reasonable effect sizes with a comparable sample size as in the offline (lab) protocol. Supplementary Information The online version contains supplementary material available at 10.1007/s40881-021-00112-w.

A new lifetime distribution called alpha power modified Weibull (APMW) distribution based on the alpha power transformation method has been studied. Various statistical properties of the newly developed distribution including quantiles, moments, stress-strength parameter, Bonferroni and Lorentz curve, residual and reversed residual lifetime function, stress-strength parameter, entropy and order statistics have been obtained. Percentage points of the APMW distribution for different values of the parameters have been obtained. The method of maximum likelihood estimation (MLE) has been used for estimating the parameters. A simulation study has been performed to evaluate the behaviour of the MLEs in terms of the sample size n and revealed that as the value of the sample size increases the value of the mean square error decreases showing the reliability of the estimators. The efficiency and flexibility of the new distribution are illustrated by analysing three real-life data sets. In each case, the APMW distribution provides a better fit indicating that the APMW distribution is a justifiable choice for fitting the considered data sets.

Abstract Aim By means of a systematic review and network meta-analysis, this study aims to answer the following questions: (a) does the placement of a biomaterial over an extraction socket lead to better outcomes in terms of horizontal and vertical alveolar dimensional changes and percentage of new bone formation than healing without coverage? And (b) which biomaterial(s) provide(s) the better outcomes? Materials and methods Parallel and split-mouth randomized controlled trials treating ≥ 10 patients were included in this analysis. Studies were identified with MEDLINE (PubMed), Embase, Cochrane Central Register of Controlled Trials, and Scopus. Primary outcomes were preservation of horizontal and vertical alveolar dimension and new bone formation inside the socket. Both pairwise and network meta-analysis (NMA) were undertaken to obtain estimates for primary outcomes. For NMA, prediction intervals were calculated to estimate clinical efficacy, and SUCRA was used to rank the materials based on their performance; multidimensional ranking was used to rank treatments based on dissimilarity. The manuscript represents the proceedings of a consensus conference of the Italian Society of Osseointegration (IAO). Results Twelve trials were included in the qualitative and quantitative analysis: 312 sites were evaluated. Autologous soft tissue grafts were associated with better horizontal changes compared to resorbable membranes. A statistically significant difference in favor of resorbable membranes, when compared to no membrane, was found, with no statistically significant heterogeneity. For the comparison between crosslinked and non-crosslinked membranes, a statistically significant difference was found in favor of the latter and confirmed by histomorphometric NMA analysis. Given the relatively high heterogeneity detected in terms of treatment approaches, materials, and outcome assessment, the findings of the NMA must be interpreted cautiously. Conclusions Coverage of the healing site is associated with superior results compared to no coverage, but no specific sealing technique and/or biomaterial provides better results than others. RCTs with larger sample sizes are needed to better elucidate the trends emerged from the present analysis. Clinical relevance Autologous soft tissue grafts and membranes covering graft materials in post-extraction sites were proved to allow lower hard tissue shrinkage compared to the absence of coverage material with sealing effect. Histomorphometric analyses showed that non-crosslinked membranes provide improved hard tissue regeneration when compared to crosslinked ones.

In order to evaluate the predictive effect of the controlled nutritional status (CONUT) score on the prognosis of patients with renal cell carcinoma (RCC) and upper urinary tract urothelial carcinoma (UTUC), a meta-analysis was performed. This systematic review has been registered on PROSPERO, the registration ID is CRD42021251879. A systematic search of the published literature using PubMed, Web of Science, Cochrane Library, EMBASE, and MEDLINE was performed. The fields of "renal cell cancer," "upper tract urothelial cancer," and "controlling nutritional status" and other fields were used as search terms. STATA 16 software was used to carry out data merging and statistical analysis of binary variables, Q test and χ2 tests were used to verify the heterogeneity between the included works of studies. Subgroup analysis and sensitivity analysis were used to explain the sources of heterogeneity between studies. Begg's test was used to assess publication bias between studies. From the first 542 studies retrieved, through strict inclusion and exclusion criteria, 7 studies finally met the requirements and were included in the meta-analysis. Pooled results indicated that high CONUT indicates worse over survival (OS) [HR = 1.70, 95% CI (1.43-2.03), P = 0.02], cancer-specific survival (CSS) [HR = 1.84, 95% CI (1.52-2.23), P = 0.01], recurrence-free survival (RFS) [HR = 1.60, 95% CI (1.26-2.03), P = 0.116], and disease-free survival (DFS) [HR = 1.47, 95% CI (1.20-1.81), P = 0.03]. Based on cancer type, cutoff value, region, and sample size, a subgroup analysis was performed. The results showed that OS and CSS were not affected by the above factors, and the high CONUT score before surgery predicted worse OS and CSS. In conclusion, this meta-analysis revealed that the preoperative CONUT score is a potential independent predictor of the postoperative prognosis of RCC/UTUC patients. A high CONUT predicts worse OS/CSS/DFS and RFS in patients.

We introduce a simulation method for dynamic portfolio valuation and risk management building on machine learning with kernels. We learn the dynamic value process of a portfolio from a finite sample of its cumulative cash flow. The learned value process is given in closed form thanks to a suitable choice of the kernel. We show asymptotic consistency and derive finite sample error bounds under conditions that are suitable for finance applications. Numerical experiments show good results in large dimensions for a moderate training sample size.

In this study we propose a sequential test for hypothesis testing on the $$C_{pmk}$$ process capability index. Furthermore, we propose a sequential sampling plan for lot acceptance based on $$C_{pmk}$$ . We compare the statistical properties of the sequential procedures with the performance of the corresponding non-sequential methodologies by carrying out an extensive simulation study. The results show that the proposed sequential methods make it possible to reach decisions much more quickly, on average, than the fixed sample size procedures with the same discriminating power.

In this paper an easy to implement method of stochastically weighing short and long-memory linear processes is introduced. The method renders asymptotically exact size confidence intervals for the population mean which are significantly more accurate than their classic counterparts for each fixed sample size n. It is illustrated both theoretically and numerically that the randomization framework of this paper produces randomized (asymptotic) pivotal quantities, for the mean, which admit central limit theorems with smaller magnitudes of error as compared to those of their leading classic counterparts. An Edgeworth expansion result for randomly weighted linear processes whose innovations do not necessarily satisfy the Cramer condition, is established. Numerical illustrations and applications to real world data are also included.

Existing procedures for testing measurement invariance focus mainly on group-level comparisons rather than individual comparisons (i.e., the main conclusion typically concerns the question of comparability of group means). We propose a set of intuitive graphical displays called Person-Level Assessment of Measurement Invariance (PLAMI), which attempt to provide information about the effect of the lack of measurement invariance on the level of person scores. This information complements the group-level information contained in model fit indices and overall effect size indices, and should be especially useful for practitioners using tests for individual diagnostics. PLAMI uses results from the multiple-group factor analysis to estimate and visualize score distortions in terms of expected score difference between members of different groups and the probability of an unacceptably high score difference, conditional for the value of latent trait. The stability of the results can be evaluated by means of resampling. Simulation can be used for an a priori evaluation of the sample size adequacy. We illustrate the proposed plots on two real examples involving testing measurement invariance of the Machiavellianism scale, and we demonstrate the added value of PLAMI compared to the use of fit indices alone.

Importance SARS-CoV-2 has infected over 200 million people worldwide, resulting in more than 4 million deaths. Randomized controlled trials are the single best tool to identify effective treatments against this novel pathogen. Objective To describe the characteristics of randomized controlled trials of treatments for COVID-19 in the United States launched in the first 9 months of the pandemic. Design, Setting, and Participants We conducted a cross-sectional study of all completed or actively enrolling randomized, interventional, clinical trials for the treatment of COVID-19 in the United States registered on www.clinicaltrials.gov as of August 10, 2020. We excluded trials of vaccines and other interventions intended to prevent COVID-19. Main Outcomes and Measures We used descriptive statistics to characterize the clinical trials and the statistical power for the available studies. For the late-phase trials (i.e., phase 3 and 2/3 studies), we compared the geographic distribution of the clinical trials with the geographic distribution of people diagnosed with COVID-19. Results We identified 200 randomized controlled trials of treatments for people with COVID-19. Across all trials, 87 (43.5%) were single-center, 64 (32.0%) were unblinded, and 80 (40.0%) were sponsored by industry. The most common treatments included monoclonal antibodies (N=46 trials), small molecule immunomodulators (N=28), antiviral medications (N=24 trials), and hydroxychloroquine (N=20 trials). Of the 9 trials completed by August 2020, the median sample size was 450 (IQR 67–1113); of the 191 ongoing trials, the median planned sample size was 150 (IQR 60–400). Of the late-phase trials (N=54), the most common primary outcome was a severity scale (N=23, 42.6%), followed by a composite of mortality and ventilation (N=10, 18.5%), and mortality alone (N=6, 11.1%). Among these late-phase trials, all trials of antivirals, monoclonal antibodies, or chloroquine/hydroxychloroquine had a power of less than 25% to detect a 20% relative risk reduction in mortality. Had the individual trials for a given class of treatments instead formed a single trial, the power to detect that same reduction in mortality would have been greater than 98%. There was large variability in access to trials with the highest number of trials per capita in the Northeast and the lowest in the Midwest. Conclusions and Relevance A large number of randomized trials were launched early in the pandemic to evaluate treatments for COVID-19. However, many trials were underpowered for important clinical endpoints and substantial geographic disparities were observed, highlighting the importance of improving national clinical trial infrastructure. Supplementary Information The online version contains supplementary material available at 10.1007/s11606-021-07167-9.

Background Face masks have been proven to be effective in protecting the public against airborne transmitted diseases when fitted appropriately. However, for homemade cloth masks and surgical masks, the fit is often poor, allowing viruses to escape through the gap. Objective This work aims to identify the correlation between the mask leakage, mask configurations, and individual’s facial features. Methods A novel locally morphing 3D face model, and a minimum-energy-based mask deployment model are used to systematically examine the mask fit for a large cohort of exemplars. Results The results show that the mask size and tuck-in ratio, along with selective facial features, especially nose height and chin length, are key factors determining the leakage location and extent. A polynomial regression model is presented for mask fitness based on localized facial features. Significance This study is a complete pipeline to test various masks on a wide range of faces with controlled modification of distinct regions of the face, which is difficult to achieve with human subjects, and provide knowledge on how the masks should be designed in the future. Impact statement The face mask “fit” affects the mask’s efficacy in preventing airborne transmission. To date, research on the face mask fit has been conducted mainly using experiments on limited subjects. The limited sample size in experimental studies makes it hard to reach a statistical correlation between mask fit and facial features in a population. Here, we employ a novel framework that utilizes a morphable face model and mask’s deployment simulation to test mask fit for many facial characteristics and mask designs. The proposed technique is an important step toward enabling personalized mask selection with maximum efficacy for society members.

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Each cluster consists of multiple subunits from which outcome data are collected. In a subunit randomization trial, subunits are randomized into different intervention arms. Observations from subunits within each cluster tend to be positively correlated due to the shared common frailties, so that the outcome data from a subunit randomization trial have dependency between arms as well as within each arm. For subunit randomization trials with a survival endpoint, few methods have been proposed for sample size calculation showing the clear relationship between the joint survival distribution between subunits and the sample size, especially when the number of subunits from each cluster is variable. In this paper, we propose a closed form sample size formula for weighted rank test to compare the marginal survival distributions between intervention arms under subunit randomization, possibly with variable number of subunits among clusters. We conduct extensive simulations to evaluate the performance of our formula under various design settings, and demonstrate our sample size calculation method with some real clinical trials.

Carefully selecting the sample size for a research study is one of the most fundamental ways to utilize resources in an ethical manner, maximize impact and replicability, and minimize research waste when investigating questions relevant to health-related quality of life (HRQOL). Despite an increased focus on sample size in the methodological literature, the topic has received limited attention in the HRQOL field, and there are still misconceptions that can weaken even well-intentioned sample size planning. This article aims to highlight common misconceptions, provide accessible and non-technical corrections to these misconceptions, and show how HRQOL researchers can benefit from a more nuanced understanding of sample size planning. Misconceptions were identified broadly through examples within the health, psychology, and HRQOL literatures. In examining these misconceptions, study-level (e.g., missing data, multilevel designs, multiple reported outcomes) and field-level (e.g., publication bias, replicability) issues relevant to HRQOL research were considered. Misconceptions include: (a) researchers should use rules of thumb or the largest sample size possible, (b) sample size planning should always focus on power, (c) planned power = actual power, (d) there is only one level of power per study, and (e) power is only relevant for the individual researcher. Throughout the article, major themes linked to these misconceptions are mapped onto recent HRQOL studies to make the connections more tangible. By clarifying several challenges and misconceptions regarding sample size planning and statistical power, HRQOL researchers will have the tools needed to augment the research literature in effective and meaningful ways.

Despite a huge advancement in neuroimaging techniques and growing importance of inter-personal brain research, few studies assess the most appropriate computational methods to measure brain-brain coupling. Here, we focus on the signal processing methods to detect brain-coupling in dyads. From a public dataset of functional Near Infra-Red Spectroscopy signals (N=24 dyads), we derived a synthetic control condition by randomization, we investigated the effectiveness of four most used signal similarity metrics: Cross Correlation, Mutual Information, Wavelet Coherence and Dynamic Time Warping. We also accounted for temporal variations between signals by allowing for misalignments up to a maximum lag. Starting from the observed effect sizes, computed in terms of Cohen’s d, the power analysis indicated that a high sample size ( $$N> 150$$ ) would be required to detect significant brain-coupling. We therefore discuss the need for specialized statistical approaches and propose bootstrap as an alternative method to avoid over-penalizing the results. In our settings, and based on bootstrap analyses, Cross Correlation and Dynamic Time Warping outperform Mutual Information and Wavelet Coherence for all considered maximum lags, with reproducible results. These results highlight the need to set specific guidelines as the high degree of customization of the signal processing procedures prevents the comparability between studies, their reproducibility and, ultimately, undermines the possibility of extracting new knowledge.

A centered $$L^2$$ -norm based test statistic is used for testing if a high-dimensional mean vector equals zero where the data dimension may be much larger than the sample size. Inspired by the fact that under some regularity conditions the asymptotic null distributions of the proposed test are the same as the limiting distributions of a chi-square-mixture, a three-cumulant matched chi-square-approximation is suggested to approximate this null distribution. The asymptotic power of the proposed test under a local alternative is established and the effect of data non-normality is discussed. A simulation study under various settings demonstrates that in terms of size control, the proposed test performs significantly better than some existing competitors. Several real data examples are presented to illustrate the wide applicability of the proposed test to a variety of high-dimensional data analysis problems, including the one-sample problem, paired two-sample problem, and MANOVA for correlated samples or independent samples.

Background Stroke, Parkinson, and multiple sclerosis are a range of diseases affecting the nervous system and show balance impairments due to damage of the balance control system. Many early articles have been published on the effect of exercise on balance in patients suffering from neuromuscular diseases. However, a comprehensive study showing a clear result of these three diseases was not found. Hence, the purpose of the present meta-analysis and systematic review is to determine the effect of exercise on balance in people with stroke, Parkinson, and multiple sclerosis. Methods According to the PRISMA 2009 multi-step instructions, keywords related to the purpose of the research were browsed in the MeSH browser databases; IranDoc, MagIran, IranMedex, SID, ScienceDirect, Web of Science (WoS), ProQuest, Medline (PubMed), Scopus, and Google Scholar were searched to extract articles published in Persian and English language. The search process for retrieving the articles in the sources mentioned from January 01, 2000, to December 30, 2020, was done. The heterogeneity index of the studies was determined using the I2 test. Given the heterogeneity, the random-effects model was used to combine the articles and the results. Results Initially, 7067 articles were found, but after removing duplicate and irrelevant articles, 96 clinical trials with a sample size of the intervention group of 1760 people were included in the study. As a result of the articles' composition, the mean balance score index after exercise in the intervention group showed a significant increase of 0.67 ± 0.12 of the unit (P˂0.01). The highest rate of increase in the balance score after the intervention was reported in patients with myelomeningocele with 1.66 ± 0.3 unit (P˂0.01). Conclusion Considering the positive effect of using exercise on increasing the balance in patients with stroke, Parkinson, and multiple sclerosis, it is recommended that health care providers implement a regular exercise program to improve the condition of these patients.

Introduction Magnitudes of change in endothelial function research can be articulated using effect size statistics. Effect sizes are commonly used in reference to Cohen’s seminal guidelines of small (d = 0.2), medium (d = 0.5), and large (d = 0.8). Quantitative analyses of effect size distributions across various research disciplines have revealed values differing from Cohen’s original recommendations. Here we examine effect size distributions in human endothelial function research, and the magnitude of small, medium, and large effects for macro and microvascular endothelial function. Methods Effect sizes reported as standardized mean differences were extracted from meta research available for endothelial function. A frequency distribution was constructed to sort effect sizes. The 25th, 50th, and 75th percentiles were used to derive small, medium, and large effects. Group sample sizes and publication year from primary studies were also extracted to observe any potential trends, related to these factors, in effect size reporting in endothelial function research. Results Seven hundred fifty-two effect sizes were extracted from eligible meta-analyses. We determined small (d = 0.28), medium (d = 0.69), and large (d = 1.21) effects for endothelial function that corresponded to the 25th, 50th, and 75th percentile of the data distribution. Conclusion Our data indicate that direct application of Cohen’s guidelines would underestimate the magnitude of effects in human endothelial function research. This investigation facilitates future a priori power analyses, provides a practical guiding benchmark for the contextualization of an effect when no other information is available, and further encourages the reporting of effect sizes in endothelial function research.

Background The identification of coronavirus disease 2019 (COVID-19) risk factors is requested to implement prevention strategies. Aim To explore the associations between the COVID-19 incidence and malnutrition, sarcopenia, and frailty, identified as potential risk factors in previous cross-sectional studies. Methods Malnutrition, sarcopenia, and frailty were assessed at the last available follow-up from the Sarcopenia and Physical Impairments with Advancing Age (SarcoPhAge) cohort (i.e., the fifth year that ended in 2019) according to the Mini-Nutritional Assessment short-form, the European Working Group on Sarcopenia in Older People (EWGSOP2), and the Fried criteria, respectively. Information regarding the COVID-19 was gathered by phone calls interviews in April 2021 to measure its self-declared incidence. Adjusted Cox regressions and Kaplan–Meier curves were performed. Results The present study included 241 participants [median age 75.6 (73.0–80.6) years, 63.1% women]. Among them, 27 participants (11.2%) developed the non-fatal Covid-19. No significant increased risks of COVID-19 were observed in patients with malnutrition [adjusted HR 1.14 (0.26–5.07)] and sarcopenia [adjusted HR 1.25 (0.35–4.42)]. Nevertheless, the incidence of COVID-19 was significantly higher in frail (44.4%) than in robust participants (8.5%) [Adjusted HR 7.01 (2.69–18.25)], which was confirmed by the Kaplan–Meier curves (p

Vancomycin is widely used in pediatric patients, however, large inter- and intraindividual variability are observed in vancomycin pharmacokinetics, affecting proper therapeutic monitoring. This review aimed at providing a comprehensive synthesis of the population pharmacokinetic models of vancomycin in pediatric patients and identifying potential factors responsible for the variability observed in various subpopulations. We conducted a literature search of the PubMed and EMBASE databases to obtain population pharmacokinetic studies for vancomycin published between January 2011 and January 2020, which resulted in a total of 33 studies. Vancomycin pharmacokinetics were generally characterized using a one-compartment model (n = 27), while a two-compartment model was used in six studies. The median (interquartile range) of the typical vancomycin clearance (CL) and the total volume of distribution adjusted to the median or mean body weight of the respective study was 0.103 L/h/kg (0.071-0.125) and 0.64 L/kg (0.59-1.03), respectively. Median weight-adjusted CL between different child age groups, such as infants and adolescents, did not appear to vary significantly, although the sample size for many age groups was very small. Examples of the conditions with relatively abnormal vancomycin pharmacokinetic values include renal insufficiency, sepsis, hematological and solid malignancy, and hypothermia treatment. Factors influencing pediatric vancomycin pharmacokinetics after adjusting for size and maturation include various renal function descriptors and some case-specific variables such as dialysate flow rate, ultrafiltrate output, and hypothermia. This review was able to document possible variables explaining the high variability observed in certain subpopulations and contrast vancomycin pharmacokinetics in different pediatric subpopulations.

As single-cell datasets grow in sample size, there is a critical need to characterize cell states that vary across samples and associate with sample attributes, such as clinical phenotypes. Current statistical approaches typically map cells to clusters and then assess differences in cluster abundance. Here we present co-varying neighborhood analysis (CNA), an unbiased method to identify associated cell populations with greater flexibility than cluster-based approaches. CNA characterizes dominant axes of variation across samples by identifying groups of small regions in transcriptional space-termed neighborhoods-that co-vary in abundance across samples, suggesting shared function or regulation. CNA performs statistical testing for associations between any sample-level attribute and the abundances of these co-varying neighborhood groups. Simulations show that CNA enables more sensitive and accurate identification of disease-associated cell states than a cluster-based approach. When applied to published datasets, CNA captures a Notch activation signature in rheumatoid arthritis, identifies monocyte populations expanded in sepsis and identifies a novel T cell population associated with progression to active tuberculosis.

Purpose Standard treatment for skeletally immature adolescents with moderate Adolescent Idiopathic Scoliosis (AIS) is a full-time spinal orthosis. However, adherence to full-time wear (≥ 18 h/day) is often challenging for these patients. Nighttime bracing is an alternative option that may improve patient adherence and/or satisfaction. This systematic review and meta-analysis assessed the effectiveness of nighttime bracing in patients with AIS. Methods A systematic review of studies evaluating nighttime bracing was performed. PubMed, Medline, Embase, CINAHL and Cochrane library databases were searched (01/1975-03/2020); two reviewers assessed eligibility. Eligible articles were peer reviewed, in English, and reported outcomes for patients who met Scoliosis Research Society (SRS) criteria. The primary outcome was curve progression ≥ 6°. Pooled progression rates were calculated from random effects meta-analyses with inverse-variance weights; 95% CIs were calculated. Results Nine studies (n = 595) were included. The overall pooled progression rate to ≥ 6° was 40.7% (95% CI: 30.4-51.5%). The pooled progression rate to surgical magnitude was 24.8% (95% CI: 4.5-53.6%). The most successful outcomes were in subjects with thoracolumbar/lumbar curves and subjects who initiated bracing at Risser 1/2 (pooled progression rates were 27.8% (95% CI: 17.0-40.0%) and 16.5% (95% CI: 11.7-21.8%), respectively). Univariate sub-analyses were conducted due to sample sizes. Conclusions Progression rates in patients with primary thoracolumbar/lumbar curves and in patients who initiated nighttime bracing at Risser 1/2 were comparable to published progression rates for full-time bracing, indicating that nighttime bracing may be equally effective for these patients. However, the strength of these conclusions is limited by the sample size and the overall quality of included studies.

Anxiety disorders are more prevalent and severe in women than men. Extant research suggests that the menstrual cycle modulates the severity and expression of anxiety symptoms across a range of disorders. The aims of this systematic review were to synthesise the existing literature investigating menstrual phase-related fluctuations in symptoms of anxiety disorders, and related conditions PTSD and OCD, in menstruating women, and to evaluate the methodologies used. PsycINFO and PubMed were searched through to April 2021 for studies that measured and compared symptoms of a diagnosed anxiety disorder, PTSD, or OCD, between at least two menstrual phases. Fourteen studies meeting inclusion criteria were identified. The review revealed evidence for exacerbation of a broad range of symptoms in panic disorder, PTSD, social anxiety disorder, and generalised anxiety disorder, around the weeks prior to and post menses onset, coincident with elevated but declining ovarian hormones, and low hormone levels, respectively. Effects were heterogenous between individuals and different symptom types. Key methodological weaknesses included sub-optimal and inconsistent means of defining and identifying menstrual phases, low sample representativeness, and small sample sizes. Menstrual fluctuations in anxiety symptoms appear to be a feature of anxiety disorders, PTSD, and OCD, but likely only occur in a subset of women. Future research in this field could better manage and account for such heterogeneity by using group-based trajectory modelling in larger sample sizes and using pre-screening to recruit women with known histories of menstrual fluctuation in anxiety symptoms.

There is a varying prevalence of olfactory and taste dysfunction (OTD) in COVID-19 patients, with a higher prevalence reported in the European population as compared to the Asian population. Psychophysical tests are crucial to determine the exact frequency, extent and clinical characteristics of these OTDs. The present study objectively evaluated the OTD for patients treated in the Dedicated COVID-19 Hospital (DCH) in Shahdol. This prospective cross-sectional study was conducted after IEC approval in DCH Shahdol for a period of four months among RT-PCR positive patients, and they were evaluated using validated chemosensitive psychophysical test during ENT consultation to identify OTD. The sample size was calculated as 92 considering prevalence (p) of OTD as 41.3% by applying formula: n = (Z1-a/2)2 × p (1-p) / d 2. The information pertaining to the subjects was kept anonymous and confidential. During data analysis, an association was significant for p value < 0.05. It was observed that 45.5% of subjects reported either loss of taste or smell. During objective evaluation, olfactory and taste dysfunction was observed among 42.4% of subjects (95/224). The Chi-square analysis reflected statistically significant difference (p < 0.05) between subjects with OTD and without OTD for the variables such as days from onset of symptoms to admission, and symptoms (fever, sore throat and shortness of breath). Taste and smell are among the important senses and in India they are mostly subjectively evaluated for COVID-19 induced OTD which results in underreporting of these symptoms. So, a gold standard objective evaluation should be taken into consideration to evaluate OTD.

PURPOSE To estimate the prevalence of drug-related deaths with regard to total hospital mortality and to explore the heterogeneity in its estimation through subgroup analysis, univariate and multivariate analysis. METHODS Two investigators independently searched the PubMed and Google Scholar databases with appropriate key terms to identify observational and randomised studies assessing drug-related problems. The prevalence of drug-related deaths was estimated using a double arcsine method. The heterogeneity was explored through subgroup and univariate analysis for the following study characteristics: study design, age group, study ward, study region, types of drug-related problems, study duration, sample size and study period. The study variables showing significant effects were further explored through a multivariable regression model. The percentage of preventable drug-related deaths was explored as a secondary objective. RESULTS Of the 480 full-text articles assessed, 23 studies satisfying the selection criteria were included. The mean percentage of drug-related deaths was 5.6% (95% CI: 3.8-7.6%; I2 = 96%). The univariable analysis showed study design (regression coefficient: 4.31) and study wards (regression coefficient: - 6.37) as heterogeneity modifiers. In the multivariable model, only the study ward was considered a significant predictor of drug-related deaths (regression coefficient: - 5.78; p = 0.04). The mean percentage of preventable drug-related deaths was 45.2% (95% CI: 33.6-57.0%; I2 = 60%). CONCLUSION Drug-related problems are an important cause of mortality. The variability in its estimation could be explained by admission wards.

We consider the General Linear Model (GLM) which includes multivariate analysis of variance (MANOVA) and multiple linear regression as special cases. In practice, there are several widely used criteria for GLM: Wilks’ lambda, Bartlett–Nanda–Pillai test, Lawley–Hotelling test, and Roy maximum root test. Limiting distributions for the first three mentioned tests are known under different asymptotic settings. In the present paper, we obtain computable error bounds for the normal approximation of the Lawley–Hotelling statistic when the dimension grows proportionally to the sample size. This result enables us to get more precise calculations of the p-values in applications of multivariate analysis. In practice, more and more often analysts encounter situations where the number of factors is large and comparable with the sample size. Examples include medicine, biology (i.e., DNA microarray studies), and finance.

Objectives In this proof of concept study, a deep learning–based method for automatic analysis of digital mammograms (DM) as a tool to aid in assessment of neoadjuvant chemotherapy (NACT) treatment response in breast cancer (BC) was examined. Methods Baseline DM from 453 patients receiving NACT between 2005 and 2019 were included in the study cohort. A deep learning system, using the aforementioned baseline DM, was developed to predict pathological complete response (pCR) in the surgical specimen after completion of NACT. Two image patches, one extracted around the detected tumour and the other from the corresponding position in the reference image, were fed into a classification network. For training and validation, 1485 images obtained from 400 patients were used, and the model was ultimately applied to a test set consisting of 53 patients. Results A total of 95 patients (21%) achieved pCR. The median patient age was 52.5 years (interquartile range 43.7–62.1), and 255 (56%) were premenopausal. The artificial intelligence (AI) model predicted the pCR as represented by the area under the curve of 0.71 (95% confidence interval 0.53–0.90; p = 0.035). The sensitivity was 46% at a fixed specificity of 90%. Conclusions Our study describes an AI platform using baseline DM to predict BC patients’ responses to NACT. The initial AI performance indicated the potential to aid in clinical decision-making. In order to continue exploring the clinical utility of AI in predicting responses to NACT for BC, further research, including refining the methodology and a larger sample size, is warranted. Key Points • We aimed to answer the following question: Prior to initiation of neoadjuvant chemotherapy, can artificial intelligence (AI) applied to digital mammograms (DM) predict breast tumour response? • DMs contain information that AI can make use of for predicting pathological complete (pCR) response after neoadjuvant chemotherapy for breast cancer. • By developing an AI system designed to focus on relevant parts of the DM, fully automatic pCR prediction can be done well enough to potentially aid in clinical decision-making.

To evaluate how social stressors, organizational stressors, and physical strains are related to suicidal ideation (SI) at an urban police department. Data was gathered between January–February 2020. Each case of SI was matched to 4 controls based on age, gender and military experience for a total sample size of 110 officers. Conditional logistic regression models assessed the relationship between stress domains and SI. Five percent (5%) of the officers surveyed (n = 22) reported SI. After adjustment for sociodemographic characteristics, high levels of organizational stress, versus low organizational stress, were associated with 9.2 (95% CI: 1.1–75.8) times the odds of SI compared to no SI. In the fully adjusted model (i.e., sociodemographics and other stressors), medium and high levels of social stress showed 5.1 (95% CI: 1.1–23.5) and 3.8 (95% CI: 1.0–14.5) higher odds of SI compared to no SI. The likelihood of SI increased incrementally as higher number of stressors were reported, suggesting a significant dose-response relationship. This study found organizational and social stress were the strongest predictors of SI for law enforcement officers, as opposed to physical strain. This study serves to further inform the multi-dimensionality of police stress pathways to advise department psychological prevention efforts.

Microscopic examination of stained peripheral blood smears is, nowadays, an indispensable tool in the evaluation of patients with hematological and non-hematological diseases. While a rapid automated quantification of the regular blood cells is available, recognition and counting of immature white blood cells (WBC) still relies mostly on the microscopic examination of blood smears by an experienced observer. Recently, there are efforts to improve the prediction by various machine learning approaches. An open dataset collection including the recently digitalized single-cell images for 200 patients, from peripheral blood smears at 100 × magnification, was used. We studied different morphological, fractal, and textural descriptors for WBC classification, with an aim to indicate the most reliable parameters for the recognition of certain cell types. Structural properties of both the mature and non-mature leukocytes obtained from (i) acute myeloid leukemia patients, or (ii) non-malignant controls, were studied in depth, with a sample size of about 25 WBC per group. We quantified structural and textural differences and, based on the statistical ranges of parameters for different WBC types, selected eight features for classification: Cell area, Nucleus-to-cell ratio, Nucleus solidity, Fractal dimension, Correlation, Contrast, Homogeneity, and Energy. Classification Precision of up to 100% (80% on average) was achieved.

We consider a random censoring model for survival analysis, allowing the possibility that only a proportion of individuals in the population are susceptible to death or failure, and the remainder are immune or cured. Susceptibles suffer the event under study eventually, but the time at which this occurs may not be observed due to censoring. Immune individuals have infinite lifetimes which are always censored in the sample. Assuming that the distribution of the susceptibles’ lifetimes as well as the censoring distribution have infinite right endpoints and are in the domain of attraction of the Gumbel distribution, we obtain asymptotic distributions, as sample size tends to infinity, of statistics relevant to testing for the possible existence of immunes in the population.

The extrapolation of quantiles beyond or below the largest or smallest observation plays an important role in hydrological practice, design of hydraulic structures, water resources management, or risk assessment. Traditionally, extreme quantiles are obtained using parametric methods that require to make an a priori assumption about the distribution that generated the data. This approach has several limitations mainly when applied to the tails of the distribution. Semiparametric or nonparametric methods, on the other hand, allow more flexibility and they may overcome the problems of the parametric approach. Therefore, we present here a comparison between three selected semi/nonparametric methods, namely the methods of Hutson (Stat and Comput, 12(4):331–338, 2002) and Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) and kernel density estimation. While the first and third methods have already applications in hydrology, Scholz (Nonparametric tail extrapolation. Tech. Rep. ISSTECH-95-014, Boeing Information and Support Services, Seattle, WA, United States of America, 1995) is proposed in this context for the first time. After describing the methods and their applications in hydrology, we compare their performance for different sample lengths and return periods. We use synthetic samples extracted from four distributions whose maxima belong to the Gumbel, Weibull, and Fréchet domain of attraction. Then, the same methods are applied to a real precipitation dataset and compared with a parametric approach. Eventually, a detailed discussion of the results is presented to guide researchers in the choice of the most suitable method. None of the three methods, in fact, outperforms the others; performances, instead, vary greatly with distribution type, return period, and sample size.

Seemingly unrelated mixed-effects models were used to model biomass models for Larix olgensis, Pinus koraiensis, and Pinus sylvestris, and the developed models considered multiple variables to enhance the practicality of the models. Korean larch (Larix olgensis), Korean pine (Pinus koraiensis), and Mongolian pine (Pinus sylvestris) are important coniferous species in the northeastern area of the P.R. China; hence, accurate biomass estimates of these species are meaningful for evaluating forest health and calculating carbon storage. Based on the tree variables of diameter at breast height ( $$DBH$$ ), tree height ( $$H$$ ), age, live crown length and crown width, three log-transformed species-specific biomass functions were developed using linear seemingly unrelated regression (SUR) since the error structures of the component biomass model (stem, branches, foliage and roots) were proven to be multiplicative. Furthermore, the plot-level random effect was introduced into the SUR model, namely the SURM model, to achieve more accurate biomass estimates. The results showed that the SURM model has a better fitting performance than the SUR models for three species and model types. The determination coefficient, $${R}^{2}$$ , was always larger for the SURM models than for the corresponding SUR models, while the root mean square error, $${\text{RMSE}}$$ , was always smaller for the SURM models. By leave-one-out validation, the SURM models were proven to provide more accurate predictions because the mean prediction percent error ( $${\text{MPE}}$$ ) was close to 0 and the mean absolute percentage error ( $${\text{MAPE}}$$ ) was smaller than that of the corresponding SUR models across species and model types. In addition, the size and design of the sample used to calibrate for random effects were assessed using the $${\text{MAPE}}$$ statistical index, $${\text{MAPE}}$$ . The calibration of the SURM showed a decreasing pattern of bias as the sample size increased, indicating that more available sampled trees improved the prediction accuracy of SURM models, while only slight differences existed across sampling designs. Overall, the newly developed biomass models have advantages in various data structures, and will be meaningful and useful in terms of accurately predicting the biomass of three important species in Northeast China.

In China, rural–urban migration is one of major influences on the mental health of migrant and left-behind children. Literature suggests that the perception of discrimination is an important factor that influences the mental health of these children. The present research explores (1) whether migrant children and left-behind children are different in the relationship between the perception of discrimination and mental health, and (2) whether the relationship between the perception of discrimination and mental health of these children is moderated by gender and age. Using a meta-analytic technique, the authors included 26 studies (generating 48 independent samples) with a total sample size of 28,883 participants. Results showed that the perception of discrimination of migrant children was negatively correlated with positive indicators of mental health, and it has a stronger effect than left-behind children; the perception of discrimination of migrant children was positively correlated with negative indicators of mental health, and it has a weaker effect than left-behind children. Additionally, gender moderated the relationship between the perception of discrimination and the positive indicators of mental health among left-behind children, while age moderated such relationship among migrant children.

The goal of this review was to provide an update on the literature examining how voluntary, temporary abstention from eating impacts cognitive function. We evaluated peer-reviewed articles published between August 2013 and January 2021 that assessed adults, included a measure of cognitive functioning with neutral stimuli, and compared individuals in a fasted state to individuals in a fed state (either within- or between-subject designs). Nineteen articles (21 studies) met inclusion criteria. Sample sizes, fasting methods, and tasks varied across studies. Review of studies indicated that fasting was associated with deficits in cognitive functioning; few studies indicated a benefit in cognitive functioning following a single voluntary fast. The heterogeneity and rarity of available studies limits the conclusions that can be drawn. Several crucial psychosocial and sociodemographic moderators remain unexplored. Recommendations for future work are discussed.

OBJECTIVE This study aimed to systematically identify and synthesize evidence on the feasibility, acceptability, and effects of behavior change interventions for improving multiple dietary behaviors among cancer survivors. METHODS A total of 14 electronic databases and three trial registries were searched. Experimental studies that examined the feasibility, acceptability, and effects of behavior change interventions for improving multiple dietary behaviors among cancer survivors and published in English or Chinese peer-reviewed journals or protocols were considered eligible. The methodological quality of the included studies was evaluated using the revised Cochrane risk-of-bias assessment tool. Data were extracted and synthesized narratively. RESULTS Six studies, with a sample size ranging from 50 to 3088, were included. The studies had a high overall risk of bias. Six studies reported feasibility data, and the average eligibility, recruitment, and retention rates at post-intervention were 60.7%, 66.7%, and 90.7%, respectively. Only one study measured the acceptability and reported that 66.6% of participants were satisfied with the intervention. Five out of the six studies that measured fruit and vegetable consumption reported statistically significant positive intervention effects. Two studies reported inconsistent intervention effects on wholegrain consumption. Only one study measured the consumption of processed meat, sugar, and alcohol, which had statistically nonsignificant intervention effect. CONCLUSIONS Behavior change interventions for improving multiple dietary behaviors might be feasible and effective to increase fruit and/or vegetable consumption among cancer survivors. Further research is needed to examine the acceptability and effects of the intervention for modifying other dietary behavior.

Introduction Time-to-event data from clinical trials are routinely extrapolated using parametric models to estimate the cost effectiveness of novel therapies, but how this approach performs in the presence of heterogeneous populations remains unknown. Methods We performed a simulation study of seven scenarios with varying exponential distributions modelling treatment and prognostic effects across subgroup and complement populations, with follow-up typical of clinical trials used to appraise the cost effectiveness of therapies by agencies such as the UK National Institute for Health and Care Excellence (NICE). We compared established and emerging methods of estimating population life-years (LYs) using parametric models. We also proved analytically that an exponential model fitted to censored heterogeneous survival times sampled from two distinct exponential distributions will produce a biased estimate of the hazard rate and LYs. Results LYs are underestimated by the methods in the presence of heterogeneity, resulting in either under- or overestimation of the incremental benefit. In scenarios where the overestimation of benefit is likely, which is of interest to the healthcare provider, the method of taking the average LYs from all plausible models has the least bias. LY estimates from complete Kaplan–Meier curves have high variation, suggesting mature data may not be a reliable solution. We explore the effect of increasing trial sample size and accounting for detected treatment–subgroup interactions. Conclusions The bias associated with heterogeneous populations suggests that NICE may need to be more cautious when appraising therapies and to consider model averaging or the separate modelling of subgroups when heterogeneity is suspected or detected. Supplementary Information The online version contains supplementary material available at 10.1007/s40273-021-01082-x.

This study was to explore the value of data mining model in evaluating the treatment effect of acupuncture on patients with cervical spondylosis (CS) and neck pain. A total of 270 patients with CS and neck pain recruited in the acupuncture clinic of Shanxi Provincial People’s Hospital were selected as the research objects in this study and were divided into an acupuncture needle group (group A), an acupoint latent acupuncture group (group B), and a puncture bloodletting group (group C) randomly, with 90 cases in each group. The Northwick Park Questionnaire (NPQ), McGill Pain Questionnaire (MPQ), and Role-Physical (RP), Physiological Function (PF), General Health (GH), and Body Pain (BP) of all patients were recorded before treatment, at the completion of the fifth acupuncture, at the end of treatment, one-month follow-up, two-month follow-up, and three-month follow-up to analyse the clinical treatment effect. Based on the artificial neural network (ANN) algorithm, a curative effect evaluation method and data mining model was further established to compare the accuracy rate of data processing by different data models, and the data processed by the data mining model were compared with the clinical data to analyse the feasibility of the data mining model. The test results found that the NPQ and MPQ values of patients in group B were significantly lower than those in groups A and C (P

Identifying the geographical scale at which natural populations structure themselves is essential for conservation. One way to gauge this structure is by estimating local effective population size (Ne) and the associated measure of effective number of breeders (Nb), as the smaller and more isolated natural populations are, the smaller Ne and Nb they will present. However, as Ne and Nb are greatly influenced by demographic events and by both species’ behavior and biology, assessing the effectiveness of sample design is necessary to ensure the reliability of said estimates. Here, we first test the sample size effect on yearly Nb and generational Ne estimates from a lemon shark Negaprion brevirostris nursery in Bimini (The Bahamas) and subsequently compare these parameters to estimates of the minimal number of breeders based on pedigree reconstruction. We found that yearly estimates of Nb are positively correlated to annual variations in number of breeders estimated via pedigree reconstructions. Moreover, we measured that 30 individuals from a single cohort were sufficient to obtain reliable yearly estimates of Nb in Bimini’s lemon sharks. We then estimated generational Ne in 10 lemon shark nurseries across the Western Atlantic. Almost every nursery sampled represents an independent population on a generational time scale, with Ne rarely higher than 100 individuals. Our study reveals strong local population structure in lemon sharks, and thus their exposure to localized depletion or extirpation, suggesting that studies of coastal shark nursery areas could routinely estimate Ne and Nb to obtain management-relevant information on adult populations. .

Performance evaluation of low-volume road (LVR) at the network level is a daunting task that requires considerable time and resources due to a lack of robust and cost-effective techniques. This study primarily focused on developing an approach for establishing statistical limits in maximum allowable deflections (MADs) for a selected LVR network based on sample size estimation for each test station and estimating the stiffness values at the network level. A series of field and laboratory experimental investigations was performed using light-weight deflectometer (LWD) on selected pavement sections, categorized into four groups based on the multiple contracts with a distinct source of layer materials. Layer-wise LWD responses were captured at field and laboratory on 529 acceptance test stations and 49 calibration strips. The typical range of deflection values obtained from these test sections and calibration strips of all groups varies from 0.040 to 0.359 mm and 0.046 to 0.365 mm. Statistical analysis in terms of ANOVA and descriptive estimates was carried out for the captured deflection values. The sample size for test locations of each test station was calibrated from three to seven to minimize the significant variation among population and sample mean. Further, the computed Z-scores were considered for calculating MAD values of each layer in a group. The MAD values suggested for the selected network can compare with the measured deflection at individual test stations to identify the maintenance requirements. Thus, this study proposes a quick and robust performance evaluation and quality control mechanism to substitute conventional practices.

The aim of this systematic review and meta-analysis is to determine the frequency of plagiarism in scientific papers estimated from publications that use text-matching software to identify plagiarism. For this purpose, a literature search of 39 bibliographic databases has been conducted and a total of 10,005 articles have been identified. Ten articles met the criteria for inclusion in the meta-analysis and they checked for plagiarism in 6459 already published articles or manuscripts submitted to journals or conferences. All articles assessed plagiarism in a two-step process, first identifying textual similarity based on text-matching software and second, additionally inspecting detected similarity in the human verification process. The result revealed that 18% (95% CI: 12–25%) of articles have instances of plagiarism. Subgroup analyses were conducted to explain the large variance in the results. Following factors were tested: the number of plagiarism criteria implemented during the human verification process, sample size, the country where the study was conducted, the scientific discipline of analyzed papers, and publication status of analyzed papers. Plagiarism rates were higher across studies with a smaller sample size (N

Missing data is a common occurrence in confirmatory factor analysis (CFA). Much work had evaluated the performance of different techniques when all observed variables were either continuous or ordinal. However, few have investigated these techniques when observed variables are a mix of continuous and ordinal variables. This study investigated the performance of four approaches to handling missing data in these models: a joint ordinal-continuous full information maximum likelihood (FIML) approach and three multiple imputation approaches (fully conditional specification, fully conditional specification with latent variable formulation, and expectation-maximization with bootstrapping) combined with the weighted least squares with mean and variance adjustment (WLSMV) estimator. In a Monte-Carlo simulation, the FIML approach produced unbiased estimations of factor loadings and standard errors in almost all conditions. Fully conditional specification combined with WLSMV was second best, producing accurate estimates when the sample size was large. However, FIML encountered slight non-convergence issues when certain ordinal categories have extremely low frequencies, which is typical of skewed data. If the sample is large, fully conditional specification combined with weighted least squares is recommended when the FIML approach is not feasible (e.g., non-convergence, impractical computation durations, and variables that predict missingness are not of interest to the analysis).

Sepehrifar and Yarahmadian (Stat Pap 58:1115–1124, 2017) had developed a non-parametric test for testing exponentiality against decreasing renewal dichotomous Markov noise shock model (DRDMNS) alternatives which when subjected to scrutiny under delivers. Hence, we propose a non-parametric test for testing exponentiality against a class of distributions belonging to DRDMNS models. The asymptotic properties of the test statistic are discussed. An exact null distribution is derived and critical values with different sample sizes are obtained. The proposed test is applied to the censored data also. The results of the Monte Carlo simulations are used to further manifest the quality of the proposed test. Finally, the proposed test is illustrated using two real data sets.

Optimal sampling strategy is vital in root studies because of the high degree of variability found in the belowground ecosystems. To inform the appropriate sample size, ninety-six cores and minirhizotrons in a subtropical evergreen broadleaved forest were used to examine diameter-related variation in the distribution of root length and volume, and assess the effects of sample size on estimation accuracy and sample representativeness. The variation coefficients of root traits estimated by the minirhizotron method were much higher than those estimated by the soil core method. Furthermore, minirhizotrons observed higher proportions of root length and volume in small diameter classes, reflecting thinner roots than those estimated by the soil core method. The Monte Carlo simulation analysis shows that, for the 0–5 mm diameter class, under a confidence level of 90% and estimation accuracy of 80%, the sample size for soil cores required to estimate the root length and volume are 5 and 12, respectively, and for minirhizotrons being 54 and over 96, respectively. The cumulative root diameter class curve analysis shows that the sample sizes required to encounter 80%, 90% and 95% of all diameter classes for soil cores are 3, 6 and 10, respectively, and for minirhizotrons being 38, 61 and 77, respectively. We conclude that due to the fractal distribution of root diameter, and the difference in sampling volume between different methods, larger sample sizes are required for minirhizotrons or larger-diameter roots than for soil cores or smaller-diameter roots to achieve better estimation accuracy and sampling representativeness.

Site-specific weed management (SSWM) can provide significant herbicide savings but requires the reliable detection of weed populations. In this work, the effect of sample size on sampling precision was studied in four sub-field areas of winter cereals infested with Galium aparine. Plots of 1170 m2, 4212 m2, 2592 m2 and 2880 m2 were marked and the positions of all G. aparine individuals were recorded using a precise navigation system. Mapping cells of 3 m × 3 m were arranged over experimental data and then various sampling intensities of G. aparine populations were simulated. The relative root mean square error, mean absolute percentage error (MAPE), and false negative rate were calculated and plotted against individual sampling intensities. Regressions were fitted to non-linear models. Experimental plots had relatively low mean densities of G. aparine (1.00–2.98 plants m−2) and the spatial distributions of plants were patchy in all cases. The values of all error measures decreased with increasing sampling intensity. On the basis of the fitted model, keeping MAPE below 30% required the sampling of 32.2% of the total area for the low-density population and 13.4% for the highest-density population. The results presented demonstrate that reliable sampling of low-density populations requires a very high sampling intensity and could be helpful in the design of reliable sampling strategies for SSWM.

Background This study was an update on the AAOS clinical practice guideline’s analysis of the natural history of developmental dysplasia of the hip (DDH). The objective was to delineate the natural history of clinical instability or radiologic abnormalities of the hip in infants by identifying the proportion of cases that resolved without treatment compared to cases that progressed and/or required treatment. Methods We performed a literature search of PUBMED to identify studies which evaluated the natural history of DDH. We used the same search strategy as that utilized in the previous AAOS guidelines, updated to include articles published between September 2013 and May 2021. We assessed the quality of included articles using the Oxford Centre for Evidence-Based Medicine level of evidence and reported study demographics and outcomes using summary statistics. Results Twenty-four articles met our eligibility criteria. Most included studies were retrospective (14/24), investigated either the incidence of DDH (8/24) or assessed screening programs (7/24). The most prevalent study population followed were Graf 2A hips (7/24). Most studies were low quality with level of evidence 3 (13/24) or 4 (7/24). Sample sizes ranged from 9 to 3251. Twenty studies reported the number of cases resolved over the follow-up period with a mean rate of 84.3% (95% confidence interval 76.1, 92.6). Conclusion We found most mild-to-moderate DDH can resolve without treatment in early infancy, especially in physiologically immature (Graf 2A) hips. More high-quality evidence is needed to properly assess the natural history of DDH as only one included study was a randomized trial.

For the general linear hypothesis testing problem for high-dimensional data, several interesting tests have been proposed in the literature. Most of them have imposed strong assumptions on the underlying covariance matrix so that their test statistics under the null hypothesis are asymptotically normally distributed. In practice, however, these strong assumptions may not be satisfied or hardly be checked so that these tests are often applied blindly in real data analysis. Their empirical sizes may then be much larger or smaller than the nominal size. For these tests, this is a size control problem which cannot be overcome via purely increasing the sample size to infinity. To overcome this difficulty, in this paper, a new normal-reference test using the centralized $$L^2$$ -norm based test statistic with three cumulant matched chi-square approximation is proposed and studied. Some theoretical discussion and two simulation studies demonstrate that in terms of size control, the new normal-reference test performs very well regardless of if the high-dimensional data are nearly uncorrelated, moderately correlated, or highly correlated and it outperforms two existing competitors substantially. Two real high-dimensional data examples motivate and illustrate the new normal-reference test.

While long described in anecdotal accounts of the lived experiences of autistic individuals, the phenomenon of behaving in ways that appear inconsistent with the presence of autism (or passing as non-autistic; PAN) has recently seen a dramatic increase in scrutiny in the published scientific literature. Increased research attention has coincided with a proliferation of methods, definitions, measures, and population assumptions associated with PAN. To date, however, no review has sought to systematically identify and synthesize the literature on PAN. This systematic review reflects the state of the PAN literature as of May 2020. Ninety articles were screened, 66 were identified for evaluation, and 46 met inclusion criteria and were reliably coded for study characteristics and participant characteristics. Results reveal that the PAN literature includes a relatively even mix of qualitative, quantitative, and mixed-method studies, and that a variety of terms are used for PAN (with masking and camouflage being the most frequent). Sample sizes varied widely (from one to 832 participants), with 63.06% of participants being categorized as autistic. Nearly all studies reported methods for confirming autism diagnoses, with community and clinical diagnoses being most common. The majority of studies reported participant gender, with slightly more females included than males on average, but fewer than half of all studies compared PAN across genders. Nearly all studies reported participant age, demonstrating a wide range of 2 to 79 years, with a mean age of 23.85. Conversely, only 23.91% of studies provided participant race or ethnicity data. Nearly all studies formally or informally excluded participants with intellectual disability. Finally, measures of internalizing symptoms, which are often thought to be linked to PAN, were reported in only 17.4% of studies. Implications for gaps in understanding of PAN and future directions for the field are discussed.

Background Click-evoked auditory brainstem response (ABR) alterations are associated with autism spectrum disorder (ASD), but the specificity of these findings to the disorder is unclear. We therefore performed a meta-analysis on ABRs and attention-deficit/hyperactivity disorder (ADHD), a neurodevelopmental disorder that shares some etiologic and symptom overlap with ASD. Methods Seven papers compared ABR latency components (I, III, V, I-III, III-V, and I-V) between participants with and without ADHD. We used random-effects regression to generate component-specific estimates (Hedges's g) that adjusted for study sample sizes and the number of studies contributing to each estimate. We compared these estimates to our recently published meta-analysis of ABRs and ASD. Results All ADHD studies employed cross-sectional designs. ADHD was associated with longer latencies for waves III and V (g = 0.6, 95% confidence interval (CI) 0.3, 1.0 and g = 0.6, 95% CI 0.3, 0.9) and waves I-III and I-V (g = 0.7, 95% CI 0.2, 1.3 and g = 0.6, 95% CI 0.3, 1.0). Effect sizes from the ASD and ADHD meta-analyses did not differ from each other. Conclusions Similar patterns of ABR alterations are observed in ADHD and ASD. However, studies rarely screen for middle ear dysfunction or hearing loss and rely upon cross-sectional designs. Addressing these issues will inform the viability of ABRs as a prognostic and/or etiologic biomarker for these disorders. Impact Click-evoked ABR alterations are associated with ASD, but the specificity of these findings to the disorder is unclear. We therefore performed a meta-analysis of the association between ABRs and ADHD, a disorder that shares some etiologic and symptom overlap with ASD. ADHD was associated with longer ABR latencies for several components. These components are identical to those implicated in ASD. Effect sizes were similar in magnitude across disorders. The viability of ABRs as prognostic and/or etiologic biomarkers for neurodevelopmental risk requires addressing limitations in the literature (e.g., cross-sectional data, non-standardized ABR protocols, minimal characterization of symptom heterogeneity).

The purpose of this study is to analyze the effect of online education, which has been extensively used on student achievement since the beginning of the pandemic. In line with this purpose, a meta-analysis of the related studies focusing on the effect of online education on students' academic achievement in several countries between the years 2010 and 2021 was carried out. Furthermore, this study will provide a source to assist future studies with comparing the effect of online education on academic achievement before and after the pandemic. This meta-analysis study consists of 27 studies in total. The meta-analysis involves the studies conducted in the USA, Taiwan, Turkey, China, Philippines, Ireland, and Georgia. The studies included in the meta-analysis are experimental studies, and the total sample size is 1772. In the study, the funnel plot, Duval and Tweedie's Trip and Fill Analysis, Orwin's Safe N Analysis, and Egger's Regression Test were utilized to determine the publication bias, which has been found to be quite low. Besides, Hedge's g statistic was employed to measure the effect size for the difference between the means performed in accordance with the random effects model. The results of the study show that the effect size of online education on academic achievement is on a medium level. The heterogeneity test results of the meta-analysis study display that the effect size does not differ in terms of class level, country, online education approaches, and lecture moderators.

To evaluate the use, results, and reporting of patient-reported outcome measures specific to patients undergoing inguinal hernia repair. A systematic review was performed and reported according to the PRISMA 2020 statement. A protocol was registered at PROSPERO (CRD42021243468). Systematic searches were performed in PubMed and EMBASE. We only included randomized controlled trials that involved postoperative administration of a hernia-specific patient-reported outcome measure. Risk of bias was evaluated with the Cochrane risk of bias-tool 2.0. Twenty trials and four different instruments were included: the Carolinas Comfort Scale (nine studies), Activities Assessment Scale (six studies), Inguinal Pain Questionnaire (seven studies), and Surgical Pain Scales (one study). Included trials used patient-reported outcome measures and compared either different surgical approaches (11 studies), types of mesh/fixation (seven studies), or types of anesthesia/analgesia (two studies). Results were reported using several different methods including means, medians, or proportions of either overall results, results from subscales, or results from single questionnaire items. Seven of the 20 included studies specified a patient-reported outcome measure as a primary outcome and provided clear reporting of sample size calculation. Reporting of results from patient-reported outcome measures in inguinal hernia research was characterized by heterogeneity. The results were reported using several different methods, which impedes proper evidence synthesis. Only half of the included studies applied a patient-reported outcome measure as primary outcome. Ultimately, the heterogeneity in outcome reporting is an important methodological problem obstructing the full utilization of patient-reported outcome measures in inguinal hernia research.