Score Estimation Research Articles

Diallel crosses in Picea abies VI. Genetic variation in early reproduction in three young Norway spruce progeny trials

Abstract Female flowering and cone production took place in three Norway spruce progeny tests at ages 17 and 20 years, each planted with full-sib families from a half diallel. The number of cones on individual trees were scored in five classes. More than 50 % of the trees produced cones, and a considerable variation was found among families for the ability to produce cones (precocity) and for the number of clones scored in classes (fruitfulness). Both traits were strongly related to tree heights and diameters at the individual and at the family level. In general, tall trees produced the highest number of cones. However, some families produced many cones even if their average heights were low. In two of the half diallels, estimates of GCA variance components for the number of cones produced had twice the value of the SCA component, indicating additive genetic inheritance of cone production. Heritability estimates of cone scores were 0.10, 0.17 and 0.23, and the genetic correlations between cone production and tree heights were 0.40, 0.50 and 0.35 in the tree half-diallels, respectively.

HKDC1 enhances the proliferation, migration and glycolysis of pancreatic adenocarcinoma and is linked to immune infiltration.

Background: Understanding the molecular mechanisms of pancreatic adenocarcinoma (PAAD) development is vital for treating this disease, as the current prognosis and treatment options are highly discouraging. Objective: This study aimed to examine the involvement of Hexokinase Domain Containing 1 (HKDC1) in the progression of PAAD. Methods: The study utilized bioinformatics techniques to evaluate the relationship between the expression of HKDC1 and clinical characteristics. In vitro experiments were conducted to investigate the molecular mechanisms and biological functions of HKDC1 in PAAD. Results: The findings of this research indicate that the expression of HKDC1 was increased in various types of human cancers, and a significant correlation was observed between elevated HKDC1 expression in PAAD and unfavorable prognosis. According to the findings from univariate and multivariate Cox regression analyses, HKDC1 could potentially serve as a standalone prognostic indicator for individuals diagnosed with PAAD. After performing calculations, we determined that the HKDC1 high-expression group exhibited lower immunologic score and higher ESTIMATE score, indicating a difference in immune infiltration score. In order to validate the expression of HKDC1 in PAAD cell lines, we analyzed the PAAD cell lines through qPCR and protein blotting. The expression of HKDC1 in human PAAD tissues was also detected by western blotting. Additionally, we explored the involvement of HKDC1 in PAAD by conducting experiments such as colony formation, 5-ethynyl-2'-deoxyuridine (EdU), transwell, and wound healing assays. In our study, we discovered that disruption of HKDC1 expression in PAAD cell types resulted in a decrease in cell growth rate and inhibited cell movement and invasion. Conclusion: To conclude, our findings indicate that HKDC1 has a significant impact on the tumor microenvironment (TME) of PAAD and could potentially be a promising target for PAAD treatment, offering fresh perspectives on the management of PAAD.

Impact of the COVID-19 pandemic on the complete rehabilitation journey of hip fracture patients in Italy: From surgical admission to rehabilitation facility discharge.

The COVID-19 pandemic led to a significant reorganization of health services, potentially affecting the quality of care for major public health concerns such as proximal femoral fractures. This study aimed to investigate potential changes in the timing of various steps in the patient journey after a hip fracture during the pandemic in Piedmont, a region in Northern Italy. A retrospective study was conducted on the discharge records of patients aged 65 or older who were admitted for hip surgery following a femur fracture in 2019 and 2020. The study examined four-time steps: duration from hospital admission to surgery, length of hospital stay, interval between hospital discharge and admission to the rehabilitation facility, and duration of stay at the rehabilitation facility. To mitigate biases linked to sex and age factors, groups well-balanced across 2019 and 2020 were created using propensity score estimation. The dataset consisted of two cohorts of 583 patients each for the years 2019 and 2020. The average duration from admission to surgery was approximately 1.9 days in both years, with 75% of patients undergoing surgery within 2 days of hospital admission. The average hospital stay reduced from 13.49 days in 2019 to 11.34 days in 2020. The gap between hospital discharge and admission to rehabilitation was approximately 10-12 days, and the average duration of stay at the rehabilitation facility was about 31.6 days. The study indicates that healthcare systems can exhibit resilience and adaptability, even during a global pandemic, to ensure high-quality and safe standards of care. However, further long-term studies are needed to fully understand the pandemic's impact on primary health outcomes following hip replacement surgery and subsequent rehabilitation. The potential role of telemedicine in reducing the time between steps also warrants further investigation.

Propensity Score Weighting Analysis of Survival Outcomes Using Pseudo-Observations

Survival outcomes are common in comparative effectiveness studies and require unique handling because they are usually incompletely observed due to right-censoring. A ``once for all'' approach for causal inference with survival outcomes constructs pseudo-observations and allows standard methods such as propensity score weighting to proceed as if the outcomes are completely observed. For a general class of model-free causal estimands with survival outcomes on user-specified target populations, we develop corresponding propensity score weighting estimators based on the pseudo-observations and establish their asymptotic properties. In particular, utilizing the functional delta-method and the von Mises expansion, we derive a new closed-form variance of the weighting estimator that takes into account the uncertainty due to both pseudo-observation calculation and propensity score estimation. This allows valid and computationally efficient inference without resampling. We also prove the optimal efficiency property of the overlap weights within the class of balancing weights for survival outcomes. The proposed methods are applicable to both binary and multiple treatments. Extensive simulations are conducted to explore the operating characteristics of the proposed method versus other commonly used alternatives. We apply the proposed method to compare the causal effects of three popular treatment approaches for prostate cancer patients.

Cross-Spatiotemporal Graph Convolution Networks for Skeleton-Based Parkinsonian Gait MDS-UPDRS Score Estimation.

Gait impairment in Parkinson's Disease (PD) is quantitatively assessed using the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a well-established clinical tool. Objective and efficient PD gait assessment is crucial for developing interventions to slow or halt its advancement. Skeleton-based PD gait MDS-UPDRS score estimation has attracted increasing interest in improving diagnostic efficiency and objectivity. However, previous works ignore the important cross-spacetime dependencies between joints in PD gait. Moreover, existing PD gait skeleton datasets are very small, which is a big issue in deep learning-based gait studies. In this work, we collect a sizable PD gait skeleton dataset by multi-view Azure Kinect sensors. The collected dataset contains 102 PD patients and 30 healthy older adults. In addition, gait data from 16 young adults (aged 24-50 years) are collected to further examine the effect of age on PD gait assessment. For skeleton-based automatic PD gait analysis, we propose a novel cross-spatiotemporal graph convolution network (CST-GCN) to learn complex features of gait patterns. Specifically, a gait graph labeling strategy is designed to assemble and group cross-spacetime neighbors of the root node according to the spatiotemporal semantics of the gait skeleton. Based on this strategy, the CST-GCN module explicitly models the cross-spacetime dependencies among joints. Finally, a dual-path model is presented to realize the modeling and fusion of spatial, temporal, and cross-spacetime gait features. Extensive experiments validate the effectiveness of our method on the collected dataset.

DIFFUSION-MODEL-ASSISTED SUPERVISED LEARNING OF GENERATIVE MODELS FOR DENSITY ESTIMATION

We present a supervised learning framework of training generative models for density estimation. Generative models, including generative adversarial networks (GANs), normalizing flows, and variational auto-encoders (VAEs), are usually considered as unsupervised learning models, because labeled data are usually unavailable for training. Despite the success of the generative models, there are several issues with the unsupervised training, e.g., requirement of reversible architectures, vanishing gradients, and training instability. To enable supervised learning in generative models, we utilize the score-based diffusion model to generate labeled data. Unlike existing diffusion models that train neural networks to learn the score function, we develop a training-free score estimation method. This approach uses mini-batch-based Monte Carlo estimators to directly approximate the score function at any spatial-temporal location in solving an ordinary differential equation (ODE), corresponding to the reverse-time stochastic differential equation (SDE). This approach can offer both high accuracy and substantial time savings in neural network training. Once the labeled data are generated, we can train a simple, fully connected neural network to learn the generative model in the supervised manner. Compared with existing normalizing flow models, our method does not require the use of reversible neural networks and avoids the computation of the Jacobian matrix. Compared with existing diffusion models, our method does not need to solve the reverse-time SDE to generate new samples. As a result, the sampling efficiency is significantly improved. We demonstrate the performance of our method by applying it to a set of 2D datasets as well as real data from the University of California Irvine (UCI) repository.

Evaluation of treatment effectiveness and 10-year cardiovascular disease risk level in hypertensive outpatients

Hypertension represents a substantial risk factor for the development of cardiovascular disease (CVD), stroke, and chronic kidney disease. CVD stands as the primary cause of global mortality. This study employed a prospective cross-sectional design spanning 4 weeks. Data were collected using a pro forma designed to capture patients’ sociodemographic and clinical information. Prescribers’ adherence to anti-hypertensive prescription guidelines, as per the JNC8 guideline, and patients’ attainment of target blood pressure (BP) goals were assessed. Framingham 10-year CVD risk score estimation was employed to estimate patients’ 10-year CVD risk levels. Among the patients, 51 (59.3%) had anti-hypertensive prescriptions that adhered to the JNC8 guidelines. However, a majority, 54 (62.8%), failed to achieve their JNC8 target BP goals, and 44 (51.2%) exhibited intermediate 10-year CVD risk levels. Furthermore, 71 (98.6%) of the patients did not receive the recommended statin and/or aspirin therapy, despite its necessity. An association was found between patients’ achievement of JNC8 target BP goals and their 10-year CVD risk levels. The majority of patients received anti-hypertensive prescriptions that aligned with JNC8 guidelines. Nonetheless, most of them did not attain their JNC8 target BP goals and exhibited intermediate to high 10-year CVD risk levels.

Assessment of diabetes risk among adult population using Indian diabetes risk score and to correlate with actual capillary blood sugar level: a cross sectional study

Background: Diabetes is one of the fastest growing health challenges of the 21st century, with the number of adults living with diabetes having more than tripled over the past 20 years. IDRS is pre-screening tool for assessing diabetes mellitus. It is also a non-invasive procedure and less time consuming. Aim were to assess diabetes risk among adult urban population of district Etawah by using Indian Diabetes Risk Score and to correlate it with actual capillary blood sugar status. Methods: The present study was a cross sectional study carried out among 200 adult urban non diabetic population the age group of 30 years and above. The data was collected using predesigned questionnaire followed by capillary blood sugar estimation. The data was collected under following broad heading: socio-demographic profile, Behavioural factor, Indian diabetes risk score and capillary blood sugar estimation. Results: Majority of the participants were in the age group of 35-49 years and Hindu by religion. 62.5% gave history of fast food consumption, nearly half of the participants gave the history of moderate to heavy physical exercise. It was found that 89% of the participants fall under the category of intermediate and high risk category. On application of test of association between IDRS score and actual capillary blood sugar level, strong association exist between the two variables. Conclusions: The present study concludes that IDRS is a powerful tool for screening of high risk group of diabetes. There is a strong association between capillary blood sugar and IDRS score.

Using Signal Features of Functional Near-Infrared Spectroscopy for Acute Physiological Score Estimation in ECMO Patients.

Extracorporeal membrane oxygenation (ECMO) is a vital emergency procedure providing respiratory and circulatory support to critically ill patients, especially those with compromised cardiopulmonary function. Its use has grown due to technological advances and clinical demand. Prolonged ECMO usage can lead to complications, necessitating the timely assessment of peripheral microcirculation for an accurate physiological evaluation. This study utilizes non-invasive near-infrared spectroscopy (NIRS) to monitor knee-level microcirculation in ECMO patients. After processing oxygenation data, machine learning distinguishes high and low disease severity in the veno-venous (VV-ECMO) and veno-arterial (VA-ECMO) groups, with two clinical parameters enhancing the model performance. Both ECMO modes show promise in the clinical severity diagnosis. The research further explores statistical correlations between the oxygenation data and disease severity in diverse physiological conditions, revealing moderate correlations with the acute physiologic and chronic health evaluation (APACHE II) scores in the VV-ECMO and VA-ECMO groups. NIRS holds the potential for assessing patient condition improvements.

Optimizing Imputation for Educational Data: Exploring Training Partition and Missing Data Ratios

The integration of machine learning in educational data analysis presents challenges regarding the availability of sufficient training data, especially in the context of high missing data ratios. These challenges arise from data partitioning practices, resulting in smaller datasets and less precise models. Behavioral scientists have increasingly incorporated machine learning into propensity score estimation, necessitating investigations into the most effective training and testing partitioning methods for machine learning-based imputation. To address this gap in the literature, our Monte Carlo experiment examines the impact of partitioning methods and missing data ratios. Simulated datasets, featuring missing ratios of 10%, 30%, 50%, and 70%, are divided into training and testing sets, ranging from 80–20 to 20–80. Results indicate that each imputation method delivers highly accurate average treatment effects. However, in the context of maintaining covariate balance across diverse conditions, complex ensemble methods outperform artificial neural networks. A real-data comparison (Study II) further underscores that the adoption of sophisticated machine learning techniques significantly enhances covariate balance. This research contributes valuable insights into the development of machine learning-based imputation methods, with a specific focus on scenarios characterized by high missing data ratios, in educational data analysis.

Carotid plaque score and ischemic stroke risk stratification through a combination of B-mode and contrast-enhanced ultrasound in patients with low and intermediate carotid stenosis.

The occurrence of ischemic stroke (IS) is closely related to the characteristics of carotid plaque (CP). Due to the effect of stroke risk stratification based on B-mode ultrasound (US) and contrast-enhanced ultrasound (CEUS) that has not been studied in patients with low and intermediate carotid stenosis, we construct and validate a CP score and ischemic stroke risk stratification (ISRS) using a combination of B-mode and CEUS, in order to provide new convenient strategies to stratify these patients to prevent stroke. This retrospective study evaluated 705 patients with low and intermediate carotid stenosis who underwent B-mode and CEUS from November 2021 to April 2023. Qualitative B-mode and CEUS features of carotid plaques were analyzed using a univariable and multivariable logistic regression to construct the CP score. Then, we combined the CP score with Essen stroke risk score (ESRS) to develop ISRS. This study included a total of 705 patients with low and intermediate carotid stenosis, of which 394 were symptomatic patients (with a mean age of 71.03 ± 10.48 years) and 311 were asymptomatic patients (with a mean age of 65.13 ± 10.31 years). Plaque echogenicity, plaque morphology, carotid intima-media thickness in B-mode US and intraplaque neovascularization grading and perfusion pattern in CEUS were significantly associated with IS. The ISRS incorporating these five predictors and ESRS showed good discrimination and calibration in both primary cohort [area under the curve (AUC), 0.91; Hosmer-Lemeshow test, p = 0.903] and validation cohort (AUC, 0.84; Hosmer-Lemeshow test, p = 0.886). We developed an effective and practical tool to identify and stratify patients with low and intermediate carotid stenosis, based on the CP score and ISRS estimation. Our study may provide new insights into managing patients with no indication of surgery.

Methodological principles of building an international rating as a component of information and analytical provision of law enforcement activities

Due to the fiscal needs of a modern state, the issue of choosing methods for assessing priority and effective directions of development of law enforcement structures is becoming increasingly relevant. The purpose of this article is to justify a methodological approach, rules, modern techniques, and methods to effectively determine the rating level of the activity of a specific law enforcement structure, unit, or the system as a whole. General scientific methods (generalization, abstraction, analogy, analysis, and synthesis) and a system of statistical methods – mass observations, absolute, relative, and average comparative values, tabular, correlation-regression, and analysis of score estimates, parametric analysis, modelling, etc., were used for the study. For a comprehensive characterization of international rating assessment, a series of constructive approaches has been proposed, which can be used separately or simultaneously, depending on the need. The identification of existing types of assessment from the standpoint of qualitative and quantitative characteristics made it possible to form a legal assessment of each of them, to justify the expediency of using a universal type of law enforcement activity assessment – international rating assessment, based on the compilation of a universal indicator. This allows evaluating both qualitative and quantitative characteristics of a particular object, law enforcement system, and determining the countryʼs ranking in the overall international rating. It has been proven that comparing the results of the activities of different structural units, types of law enforcement activities, regional and international structures involves conducting both internal and external ranking. Based on the research results, it is recommended to ensure the information focus of indicators with a view to standardizing procedures, dividing them into stimulants and depressants. As one of the simplified and accessible methods for assessing the international rating, the use of a multidimensional average is proposed, the algorithm of which is described in the article. The practical value of this work lies in the fact that the proposed methodologies can be used in decision-making and justification of management decisions

The Psychometric Properties of Two Brief Measures of Teamwork in Sport.

In the current study, the structural and external validity of data derived from two shorter versions of the Multidimensional Assessment of Teamwork in Sport (MATS) were examined using multilevel analyses. Evidence of model-data fit was shown for both a 5-factor model comprising 19 items (with subscales assessing teamwork preparation, execution, evaluation, adjustments, and management of team maintenance) and a single-factor model comprising five items (providing a global estimate of teamwork). In general, data from both versions were positively and significantly correlated with (and distinct from) athletes' perceptions of team cohesion, collective efficacy, performance satisfaction, enjoyment in their sport, and commitment to their team and their coaches' transformational leadership. The measures appear well suited to detect between-teams differences, as evidenced by intraclass correlation coefficients and acceptable reliability estimates of team-level scores. In summary, the 19-item Multidimensional Assessment of Teamwork in Sport-Short and five-item Multidimensional Assessment of Teamwork in Sport-Global provide conceptually and psychometrically sound questionnaires to briefly measure teamwork in sport.

Factor score estimation in multimethod measurement designs with planned missing data.

Multimethod measurement designs with planned missing data (MMM-PMD) aim at combining cheap proxy methods (e.g., self-reports) with an expensive gold standard method (e.g., biomarker) in order to improve the cost-efficiency of research designs. This article presents a comprehensive simulation study investigating whether accurate factor scores with trustworthy confidence or credible intervals for the gold standard method can be obtained in MMM-PMD designs. The results indicate that the Bartlett-FIML, regression-FIML, and fully Bayesian estimator perform equally well in terms of recovering the true rank-order and absolute level of the factor scores for subjects with complete data. However, for subjects with planned missing data the estimated factor scores are considerably biased and cannot be computed with the Bartlett-FIML estimator. The confidence or credible intervals for the estimated factor scores are trustworthy for complete cases. For subjects with planned missing data, the coverage rates vary considerably across conditions. Thus, the present study illustrates that individual scores from MMM-PMD designs should not be used for consequential individual decisions. Still, factor scores from MMM-PMD designs might be useful when used for secondary analyses that rely on group-level statistics like computing propensity scores for matching purposes or for factor score regression. The simulation results underline that multiple reliable proxy methods that are highly correlated with the gold standard method but are not highly correlated with each other should be administered to maximize the accuracy of the estimated factor scores as well as the trustworthiness of the corresponding confidence or credible intervals. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

AI‐based Digital Biomarkers for Mild Dementia Decision Support using Mobile Playful Exercises with High Adherence

AbstractBackgroundEarly detection of cognitive decline and monitoring of cognitive functioning in mild dementia are fundamental for timely adaptation of lifestyle and intervention strategies. The development of digital dementia biomarkers through playful exercises with high adherence rate was a key objective of the national project multimodAAL (no. FFG 875345). The results of a study on computer‐based cognitive and physical training (CCPT) in persons diagnosed with mild Alzheimer’s disease (PwAD) are presented.MethodTablet‐PC‐based intervention was applied within 6 months in Austria, engaging PwADs living at home by means of playful multimodal training and activation (n = 11; female N = 8, male N = 3; age M = 76.6 / SD = 9.2 years, MMSE score M = 21.50 / SD = 4.41). PwADs interacted with a prototypical version of the BRAINMEE app that included a suite of cognitive exercises (puzzle, pairs, text gap filling) based on audiovisual information. The playful training app was introduced and assisted by mobile care professionals with weekly visits, however, PwDs played alone between these visits.ResultPwADs applied training with high adherence, finalizing M = 72 (M = 32) digital exercises per day within the first (last) month of the study. Duration of using exercise type ‘outsider’(p = .028*) and ‘quiz’ (p = .001**), averaged about 2 week figures, each provided statistically significant correlations (Spearman) with MMSE test scores, as well as ‘spot‐the‐difference’ (p = .003**) with Trail Making Test A, ‘outsider’ (p = .005**) with Auditory Verbal Learning Test (AVLT), respectively. A neural network (Support Vector Machine, linear kernel, 11‐fold cross validation) using duration of use of ‘quiz’, ’outsider’ and ‘hearing’ (guessing animal sounds) as input data resulted in M = 2.16 absolute error in MMSE score estimation on test data.ConclusionThe work outlined within the Austrian study on digital biomarker development indicates successful steps towards daily use of cognitive assessment using highly adherent playful training. The playful training app is applied in the European project MARA (no. FFG 886427) to enable continuous estimates of MCI’s mental state over time. The app was very well accepted by both PwADs and persons with MCI. It offers with its pervasive mental assessment tool a large potential for future long‐term monitoring in dementia prevention, early detection as well as in numerous dementia care services.

Seq2CASE: Weakly Supervised Sequence to Commentary Aspect Score Estimation for Recommendation

Seq2CASE: Weakly Supervised Sequence to Commentary Aspect Score Estimation for Recommendation

Amyloid‐Beta and Cerebrovascular Disease Pathology Influence White Matter Microstructure and Executive Function in a Community‐Based Pre‐Dementia Asian Cohort

AbstractBackgroundWhite matter(WM) fibres have been shown to be vulnerable to amyloid‐beta and cerebrovascular disease(CVD) (Vipin et al.,2019; Croall et al.,2017; Salthouse et al.,2011). Indeed, fractional anisotropy(FA) of corpus callosum genu has been shown to capture variability in systemic vascular health and CVD (Vemuri et al., 2018). WM alterations are also associated with memory and executive function decline(Salthouse 2011). Additionally, the CAIDE risk score estimates future dementia risk based on vascular, demographic and genetic risk factors(Kivipelto et al.,2006). However, given the high CVD load among Asians, the combined influence of such risk factors and amyloid‐beta on WM microstructure and cognition in an Asian cohort needs further exploration. Thus, we examined WM FA between low, medium and high CVD‐amyloid pathology and association with cognition in a pre‐dementia Asian cohort. We hypothesized that derogatory WM FA reduction would be greatest in the high CVD‐amyloid group and relate to worse cognition.MethodIn a community cohort of 78 pre‐dementia subjects having undergone diffusion tensor imaging (mean age:55.37 years; mean education:14.72 years), neuropsychological assessments, CAIDE derivation (CVD risk) and oligomeric amyloid‐beta from the Dementia Research Centre (Singapore), we identified low CVD‐amyloid pathology (A‐C‐,n = 24), intermediate CVD‐amyloid (A±C±,n = 46) pathology and high CVD‐amyloid (A+C+,n = 8). TRActs Constrained by UnderLying Anatomy, FreeSurfer, was used to reconstruct major white matter tracts and fibre‐based FA, a measure of white matter tract overall health, was derived.ResultIncreasing CVD‐amyloid pathology related to worsening white matter microstructure with A+C+ subjects showing lowest FA in the corpus callosum genu compared to the A‐C‐ group (Fig. 1). Furthermore, higher FA in the corpus callosum splenium related to better executive function/ working memory performance (indexed using the digit span backwards test) in the A‐C‐ group (Fig. 2). In the A±C± group, presence of either amyloid or CVD pathology related to reduced executive function performance.ConclusionIn this community cohort of pre‐dementia older adults, we illustrate associations between presence of amyloid and CVD pathology and WM microstructure and cognitive disruption, especially in fibre tracts related to vascular disease and cognitive resilience. Early interventions targeting amyloid and vascular risk factors could encourage neuroplasticity, providing a window for recovery in cognitive performance.

Participation in Conditional Cash Transfer Program During Pregnancy and Birth Weight–Related Outcomes

There is limited evidence of the association of conditional cash transfers, an important strategy to reduce poverty, with prevention of adverse birth-related outcomes. To investigate the association between receiving benefits from the Bolsa Família Program (BFP) and birth weight indicators. This cohort study used a linked data resource, the Centro de Integracao de Dados e Conhecimentos Para Saude (CIDACS) birth cohort. All live-born singleton infants born to mothers registered in the cohort between January 2012 and December 2015 were included. Each analysis was conducted for the overall population and separately by level of education, self-reported maternal race, and number of prenatal appointments. Data were analyzed from January 3 to April 24, 2023. Live births of mothers who had received BFP until delivery (for a minimum of 9 months) were classified as exposed and compared with live births from mothers who did not receive the benefit prior to delivery. Low birth weight (LBW), birth weight in grams, and small for gestational age (SGA) were evaluated. Analytical methods used included propensity score estimation, kernel matching, and weighted logistic and linear regressions. Race categories included Parda, which translates from Portuguese as "brown" and is used to denote individuals whose racial background is predominantly Black and those with multiracial or multiethnic ancestry, including European, African, and Indigenous origins. A total of 4 277 523 live births (2 085 737 females [48.8%]; 15 207 among Asian [0.4%], 334 225 among Black [7.8%], 29 115 among Indigenous [0.7%], 2 588 363 among Parda [60.5%], and 1 310 613 among White [30.6%] mothers) were assessed. BFP was associated with an increase of 17.76 g (95% CI, 16.52-19.01 g) in birth weight. Beneficiaries had an 11% lower chance of LBW (odds ratio [OR], 0.89; 95% CI, 0.88-0.90). BFP was associated with a greater decrease in odds of LBW among subgroups of mothers who attended fewer than 7 appointments (OR, 0.85; 95% CI, 0.84-0.87), were Indigenous (OR, 0.73; 95% CI, 0.61-0.88), and had 3 or less years of education (OR, 0.76; 95% CI, 0.72-0.81). There was no association between BFP and SGA, except among less educated mothers, who had a reduced risk of SGA (OR, 0.83; 95% CI, 0.79-0.88). This study found that BFP was associated with increased birth weight and reduced odds of LBW, with a greater decrease in odds of LBW among higher-risk groups. These findings suggest the importance of maintaining financial support for mothers at increased risk of birth weight-related outcomes.

Validation of the novel Eosinophilic Esophagitis Impact Questionnaire

BackgroundEosinophilic esophagitis (EoE) has a detrimental effect on health-related quality of life (HRQOL). The Eosinophilic Esophagitis Impact Questionnaire (EoE-IQ) is a novel patient-reported outcome (PRO) measure assessing the impact of EoE on HRQOL. To assess suitability of the EoE-IQ, its measurement properties were evaluated.MethodsUsing baseline and week 24 data from the pivotal, randomized, placebo-controlled, multinational phase 3 R668-EE-1774 trial (NCT03633617) of dupilumab, we evaluated EoE-IQ’s measurement properties (including reliability, construct and known-groups validity, and ability to detect change) and established the threshold for change in scores that can be considered clinically meaningful.ResultsThe analysis population comprised 239 adults and adolescents with EoE. Mean age was 28.1 (standard deviation, 13.14) years; 63.6% were male, and 90.4% were White. Reliability estimates for the EoE-IQ average score exceeded acceptable thresholds for patients who were stable as indicated by ratings of Patient Global Impression of Severity (PGIS) and Change (PGIC) (intraclass correlation coefficients, 0.75 and 0.81). Construct validity correlations with other EoE-specific PRO scores were moderate at baseline (|r|= 0.44–0.60) and moderate to strong at week 24 (|r|= 0.61–0.72). In known-groups analysis, EoE-IQ average score discriminated among groups of patients at varying EoE severity levels defined by PGIS scores. A ≥ 0.6-point reduction in EoE-IQ average score (where scores range from 1 to 5, with higher scores indicating worse HRQOL) from baseline to week 24 can be considered clinically meaningful.ConclusionsThe EoE-IQ’s measurement properties are acceptable, making it a valid, reliable measure of the HRQOL impacts of EoE among adults and adolescents.Trial registration: ClinicalTrials.gov, NCT03633617. Registered August 14, 2018, https://clinicaltrials.gov/study/NCT03633617.

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

Abstract There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite sample estimation of causal effects. We address the question: when estimating the effect of a treatment on an outcome, how much does the choice of nonparametric vs parametric estimation matter? Instead of answering this question with simulations that reflect a few chosen data scenarios, we propose a novel approach to compare estimators across a large number of data-generating mechanisms drawn from nonparametric models with semi-informative priors. We apply this proposed approach and compare the performance of two nonparametric estimators (Bayesian adaptive regression tree and a targeted minimum loss-based estimator) to two parametric estimators (a logistic regression-based plug-in estimator and a propensity score estimator) in terms of estimating the average treatment effect across thousands of data-generating mechanisms. We summarize performance in terms of bias, confidence interval coverage, and mean squared error. We find that the two nonparametric estimators can substantially reduce bias as compared to the two parametric estimators in large-sample settings characterized by interactions and nonlinearities while compromising very little in terms of performance even in simple, small-sample settings.