The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core: A culturally informed, community-engaged research (CI-CER) model to advance brain health equity.

INTRODUCTION: The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) Engagement Core was launched to advance Alzheimer's disease (AD) and AD-related dementia (ADRD) health equity research in underrepresented populations (URPs). We describe our evidence-based, scalable culturally informed, community-engaged research (CI-CER) model and demonstrate its preliminary success in increasing URP enrollment. METHODS: URPs include ethnoculturally minoritized, lower education (≤ 12 years), and rural populations. The CI-CER model includes: (1) culturally informed methodology (e.g., less restrictive inclusion/exclusion criteria, sociocultural measures, financial compensation, results disclosure, Spanish Language Capacity Workgroup) and (2) inclusive engagement methods (e.g., the Engagement Core team; Hub Sites; Community-Science Partnership Board). RESULTS: As of April 2024, 60% of ADNI-4 new in-clinic enrollees were from ethnoculturally or educationally URPs. This exceeds ADNI-4's ≥ 50% URP representation goal for new enrollees but may not represent final enrollment. DISCUSSION: Findings show a CI-CER model increases URP enrollment in AD/ADRD clinical research and has important implications for clinical trials to advance health equity. HIGHLIGHTS: The Alzheimer's Disease Neuroimaging Initiative-4 (ADNI-4) uses a culturally informed, community-engaged research (CI-CER) approach. The CI-CER approach is scalable and sustainable for broad, multisite implementation. ADNI-4 is currently exceeding its inclusion goals for underrepresented populations.

An interpretable and generalizable deep learning model for iEEG-based seizure prediction using prototype learning and contrastive learning.

Epileptic seizure prediction plays a crucial role in enhancing the quality of life for individuals with epilepsy. Over recent years, a multitude of deep learning-based approaches have emerged to tackle this challenging task, leading to significant advancements. However, the 'black-box' nature of deep learning models and the considerable interpatient variability significantly impede their interpretability and generalization, thereby severely hampering their efficacy in real-world clinical applications. To address these issues, our study aims to establish an interpretable and generalizable seizure prediction model that meets the demands of clinical diagnosis. Our method extends self-interpretable prototype learning networks into a novel domain adaptation framework designed specifically for cross-patient seizure prediction. The proposed framework enables patient-level interpretability by tracing the origins of significant prototypes. For instance, it could provide information about the seizure type of the patient to which the prototype belongs. This surpasses the existing sample-level interpretability, which is limited to individual patient samples. To further improve the model's generalization capability, we introduce a contrastive semantic alignment loss constraint to the embedding space, enhancing the robustness of the learned prototypes. We evaluate our proposed model using the Freiburg intracranial electroencephalography (iEEG) dataset, which consists of 20 patients and a total of 82 seizures. The experimental results demonstrated a high sensitivity of 79.0%, a low false prediction rate of 0.183, and a high area under the receiver operating characteristic curve (AUC) value of 0.804, achieving state-of-the-art performance with self-interpretable evidence in contrast to the current cross-patient seizure prediction methods. Our study represents a significant step forward in developing an interpretable and generalizable model for seizure prediction, thereby facilitating the application of deep learning models in clinical diagnosis.

Multi-domain improves classification in out-of-distribution and data-limited scenarios for medical image analysis.

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporate multiple domains instead of specialized ones significantly alleviates the limitations observed in specialized models. We refer to this approach as multi-domain model and compare its performance to that of specialized models. For this, we introduce the incorporation of diverse medical image domains, including different imaging modalities like X-ray, MRI, CT, and ultrasound images, as well as various viewpoints such as axial, coronal, and sagittal views. Our findings underscore the superior generalization capabilities of multi-domain models, particularly in scenarios characterized by limited data availability and out-of-distribution, frequently encountered in healthcare applications. The integration of diverse data allows multi-domain models to utilize information across domains, enhancing the overall outcomes substantially. To illustrate, for organ recognition, multi-domain model can enhance accuracy by up to 8% compared to conventional specialized models.

Bridged Treatment Comparisons: an Illustrative Application in HIV Treatment.

Comparisons of treatments, interventions, or exposures are of central interest in epidemiology, but direct comparisons are not always possible due to practical or ethical reasons. Here, we detail a fusion approach to compare treatments across studies. The motivating example entails comparing the risk of the composite outcome of death, AIDS, or greater than a 50% CD4 cell count decline in people with HIV when assigned triple versus mono antiretroviral therapy, using data from the AIDS Clinical Trial Group (ACTG) 175 (mono versus dual therapy) and ACTG 320 (dual versus triple therapy). We review a set of identification assumptions and estimate the risk difference using an inverse probability weighting estimator that leverages the shared trial arms (dual therapy). A fusion diagnostic based on comparing the shared arms is proposed that may indicate violation of the identification assumptions. Application of the data fusion estimator and diagnostic to the ACTG trials indicates triple therapy results in a reduction in risk compared to monotherapy in individuals with baseline CD4 counts between 50 and 300 cells/mm3. Bridged treatment comparisons address questions that none of the constituent data sources could address alone, but valid fusion-based inference requires careful consideration of the underlying assumptions.

An enzyme-inspired specificity in deep learning model for sleep stage classification using multi-channel PSG signals input: Separating training approach and its performance on cross-dataset validation for generalizability.

Numerous automatic sleep stage classification systems have been developed, but none have become effective assistive tools for sleep technicians due to issues with generalization. Four key factors hinder the generalization of these models are instruments, montage of recording, subject type, and scoring manual factors. This study aimed to develop a deep learning model that addresses generalization problems by integrating enzyme-inspired specificity and employing separating training approaches. Subject type and scoring manual factors were controlled, while the focus was on instruments and montage of recording factors. The proposed model consists of three sets of signal-specific models including EEG-, EOG-, and EMG-specific model. The EEG-specific models further include three sets of channel-specific models. All signal-specific and channel-specific models were established with data manipulation and weighted loss strategies, resulting in three sets of data manipulation models and class-specific models, respectively. These models were CNNs. Additionally, BiLSTM models were applied to EEG- and EOG-specific models to obtain temporal information. Finally, classification task for sleep stage was handled by 'the-last-dense' layer. The optimal sampling frequency for each physiological signal was identified and used during the training process. The proposed model was trained on MGH dataset and evaluated using both within dataset and cross-dataset. For MGH dataset, overall accuracy of 81.05 %, MF1 of 79.05 %, Kappa of 0.7408, and per-class F1-scores: W (84.98 %), N1 (58.06 %), N2 (84.82 %), N3 (79.20 %), and REM (88.17 %) can be achieved. Performances on cross-datasets are as follows: SHHS1 200 records reached 79.54 %, 70.56 %, and 0.7078; SHHS2 200 records achieved 76.77 %, 66.30 %, and 0.6632; Sleep-EDF 153 records gained 78.52 %, 72.13 %, and 0.7031; and BCI-MU (local dataset) 94 records achieved 83.57 %, 82.17 %, and 0.7769 for overall accuracy, MF1, and Kappa respectively. Additionally, the proposed model has approximately 9.3 M trainable parameters and takes around 26 s to process one PSG record. The results indicate that the proposed model demonstrates generalizability in sleep stage classification and shows potential as a feasibility tool for real-world applications. Additionally, enzyme-inspired specificity effectively addresses the challenges posed by varying montage of recording, while the identified optimal frequencies mitigate instrument-related issues.

Children Are Not Small Adults: Addressing Limited Generalizability of an Adult Deep Learning CT Organ Segmentation Model to the Pediatric Population.

Deep learning (DL) tools developed on adult data sets may not generalize well to pediatric patients, posing potential safety risks. We evaluated the performance of TotalSegmentator, a state-of-the-art adult-trained CT organ segmentation model, on a subset of organs in a pediatric CT dataset and explored optimization strategies to improve pediatric segmentation performance. TotalSegmentator was retrospectively evaluated on abdominal CT scans from an external adult dataset (n = 300) and an external pediatric data set (n = 359). Generalizability was quantified by comparing Dice scores between adult and pediatric external data sets using Mann-Whitney U tests. Two DL optimization approaches were then evaluated: (1) 3D nnU-Net model trained on only pediatric data, and (2) an adult nnU-Net model fine-tuned on the pediatric cases. Our results show TotalSegmentator had significantly lower overall mean Dice scores on pediatric vs. adult CT scans (0.73 vs. 0.81, P < .001) demonstrating limited generalizability to pediatric CT scans. Stratified by organ, there was lower mean pediatric Dice score for four organs (P < .001, all): right and left adrenal glands (right adrenal, 0.41 [0.39-0.43] vs. 0.69 [0.66-0.71]; left adrenal, 0.35 [0.32-0.37] vs. 0.68 [0.65-0.71]); duodenum (0.47 [0.45-0.49] vs. 0.67 [0.64-0.69]); and pancreas (0.73 [0.72-0.74] vs. 0.79 [0.77-0.81]). Performance on pediatric CT scans improved by developing pediatric-specific models and fine-tuning an adult-trained model on pediatric images where both methods significantly improved segmentation accuracy over TotalSegmentator for all organs, especially for smaller anatomical structures (e.g., > 0.2 higher mean Dice for adrenal glands; P < .001).

Harnessing Generalizable Real-World Ophthalmic Big Data: Descriptive Analysis of the Bodhya Eye Consortium Model for Collaborative Research.

BACKGROUND: Eye care organizations and professionals worldwide are increasingly focusing on bridging the gap between population health and medical practice. Recent advances in genomics and anthropology have revealed that most Indian groups trace their ancestry to a blend of 2 genetically distinct populations: Ancestral North Indians, who share genetic affinities with Central Asians, Middle Easterners, Caucasians, and Europeans; and Ancestral South Indians, genetically distinct from groups outside the Indian subcontinent. Studies conducted among North Indian populations can therefore offer insights that are potentially applicable to these diverse global populations, underscoring significant implications for global health. OBJECTIVE: The Bodhya Eye Consortium is a collaboration among 8 high-volume nonprofit eyecare organizations from across North India. The consortium aims to harness real-world data consistently and with assured quality for collaborative research. This paper outlines the formation of the consortium as a proposed model for controlled collaborative research among the leading eyecare organizations of North India. METHODS: We detail the creation and effective implementation of a consortium following a structured road map that included planning and assessment, establishing an exploratory task force, defining specialty areas, setting objectives and priorities, and conducting a SWOT (strengths, weaknesses, opportunities, and threats) analysis. Central to this process was a comprehensive data audit aimed at standardizing data collection across all participating organizations. RESULTS: The consortium currently comprises 9 organizations, each represented in the governance structure by the Governing Council. Scientific standards for published research are established and overseen by the Scientific Committee, while the Conflict Resolution Committee manages any unresolved disputes. The consortium's working groups, organized by various eyecare specialties, collaborate on research projects through virtual interactions. A foundational step in this process was the organizationwide data audit, which revealed that most organizations complied with accurate and standardized data collection practices. Organizations with deficiencies in data completeness developed action plans to address them. Subsequently, the consortium adopted data collection proformas, contributing to the publication of high-quality manuscripts characterized by low dropout rates. CONCLUSIONS: The collaborative research conducted by the Bodhya Eye Consortium-a group of high-volume eyecare organizations primarily from North India-offers a unique opportunity to contribute to scientific knowledge across various domains of eyecare. By leveraging the established heterogeneity of anthropological and genomic origins within the population, the findings can be generalizable, to some extent, to European, Middle Eastern, and European American populations. This access to potentially invaluable, generalizable data has significant global health implications and opens possibilities for broader collaboration. The model outlined in this descriptive paper can serve as a blueprint for other health care organizations looking to develop similar collaborations for research and knowledge sharing.

Storyteller in ADNI4: Application of an early Alzheimer's disease screening tool using brief, remote, and speech-based testing.

INTRODUCTION: Speech-based testing shows promise for sensitive and scalable objective screening for Alzheimer's disease (AD), but research to date offers limited evidence of generalizability. METHODS: Data were taken from the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) studies (N = 101, N = 46 mild cognitive impairment [MCI]) and Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4) remote digital (N = 426, N = 58 self-reported MCI, mild AD or dementia) and in-clinic (N = 57, N = 13 MCI) cohorts, in which participants provided audio-recorded responses to automated remote story recall tasks in the Storyteller test battery. Text similarity, lexical, temporal, and acoustic speech feature sets were extracted. Models predicting early AD were developed in AMYPRED and tested out of sample in the demographically more diverse cohorts in ADNI4 (> 33% from historically underrepresented populations). RESULTS: Speech models generalized well to unseen data in ADNI4 remote and in-clinic cohorts. The best-performing models evaluated text-based metrics (text similarity, lexical features: area under the curve 0.71-0.84 across cohorts). DISCUSSION: Speech-based predictions of early AD from Storyteller generalize across diverse samples. HIGHLIGHTS: The Storyteller speech-based test is an objective digital prescreener for Alzheimer's Disease Neuroimaging Initiative 4 (ADNI4). Speech-based models predictive of Alzheimer's disease (AD) were developed in the AMYPRED (Amyloid Prediction in Early Stage Alzheimer's Disease from Acoustic and Linguistic Patterns of Speech) sample (N = 101). Models were tested out of sample in ADNI4 in-clinic (N = 57) and remote (N = 426) cohorts. Models showed good generalization out of sample. Models evaluating text matching and lexical features were most predictive of early AD.

Discovery of generalizable TBI phenotypes using multivariate time-series clustering.

Traumatic Brain Injury (TBI) presents a broad spectrum of clinical presentations and outcomes due to its inherent heterogeneity, leading to diverse recovery trajectories and varied therapeutic responses. While many studies have delved into TBI phenotyping for distinct patient populations, identifying TBI phenotypes that consistently generalize across various settings and populations remains a critical research gap. Our research addresses this by employing multivariate time-series clustering to unveil TBI's dynamic intricates. Utilizing a self-supervised learning-based approach to clustering multivariate time-Series data with missing values (SLAC-Time), we analyzed both the research-centric TRACK-TBI and the real-world MIMIC-IV datasets. Remarkably, the optimal hyperparameters of SLAC-Time and the ideal number of clusters remained consistent across these datasets, underscoring SLAC-Time's stability across heterogeneous datasets. Our analysis revealed three generalizable TBI phenotypes (α, ß, and γ), each exhibiting distinct non-temporal features during emergency department visits, and temporal feature profiles throughout ICU stays. Specifically, phenotype α represents mild TBI with a remarkably consistent clinical presentation. In contrast, phenotype ß signifies severe TBI with diverse clinical manifestations, and phenotype γ represents a moderate TBI profile in terms of severity and clinical diversity. Age is a significant determinant of TBI outcomes, with older cohorts recording higher mortality rates. Importantly, while certain features varied by age, the core characteristics of TBI manifestations tied to each phenotype remain consistent across diverse populations.

Generalizable diagnosis of chest radiographs through attention-guided decomposition of images utilizing self-consistency loss.

BACKGROUND: Chest X-ray (CXR) is one of the most commonly performed imaging tests worldwide. Due to its wide usage, there is a growing need for automated and generalizable methods to accurately diagnose these images. Traditional methods for chest X-ray analysis often struggle with generalization across diverse datasets due to variations in imaging protocols, patient demographics, and the presence of overlapping anatomical structures. Therefore, there is a significant demand for advanced diagnostic tools that can consistently identify abnormalities across different patient populations and imaging settings. We propose a method that can provide a generalizable diagnosis of chest X-ray. METHOD: Our method utilizes an attention-guided decomposer network (ADSC) to extract disease maps from chest X-ray images. The ADSC employs one encoder and multiple decoders, incorporating a novel self-consistency loss to ensure consistent functionality across its modules. The attention-guided encoder captures salient features of abnormalities, while three distinct decoders generate a normal synthesized image, a disease map, and a reconstructed input image, respectively. A discriminator differentiates the real and the synthesized normal chest X-rays, enhancing the quality of generated images. The disease map along with the original chest X-ray image are fed to a DenseNet-121 classifier modified for multi-class classification of the input X-ray. RESULTS: Experimental results on multiple publicly available datasets demonstrate the effectiveness of our approach. For multi-class classification, we achieve up to a 3% improvement in AUROC score for certain abnormalities compared to the existing methods. For binary classification (normal versus abnormal), our method surpasses existing approaches across various datasets. In terms of generalizability, we train our model on one dataset and tested it on multiple datasets. The standard deviation of AUROC scores for different test datasets is calculated to measure the variability of performance across datasets. Our model exhibits superior generalization across datasets from diverse sources. CONCLUSIONS: Our model shows promising results for the generalizable diagnosis of chest X-rays. The impacts of using the attention mechanism and the self-consistency loss in our method are evident from the results. In the future, we plan to incorporate Explainable AI techniques to provide explanations for model decisions. Additionally, we aim to design data augmentation techniques to reduce class imbalance in our model.

Scoping review on natural language processing applications in counselling and psychotherapy.

Recent years have witnessed some rapid and tremendous progress in natural language processing (NLP) techniques that are used to analyse text data. This study endeavours to offer an up-to-date review of NLP applications by examining their use in counselling and psychotherapy from 1990 to 2021. The purpose of this scoping review is to identify trends, advancements, challenges and limitations of these applications. Among the 41 papers included in this review, 4 primary study purposes were identified: (1) developing automated coding; (2) predicting outcomes; (3) monitoring counselling sessions; and (4) investigating language patterns. Our findings showed a growing trend in the number of papers utilizing advanced machine learning methods, particularly neural networks. Unfortunately, only a third of the articles addressed the issues of bias and generalizability. Our findings provided a timely systematic update, shedding light on concerns related to bias, generalizability and validity in the context of NLP applications in counselling and psychotherapy.

Deep learning for tubes and lines detection in critical illness: Generalizability and comparison with residents.

Background: Artificial intelligence (AI) has been proven useful for the assessment of tubes and lines on chest radiographs of general patients. However, validation on intensive care unit (ICU) patients remains imperative. Methods: This retrospective case-control study evaluated the performance of deep learning (DL) models for tubes and lines classification on both an external public dataset and a local dataset comprising 303 films randomly sampled from the ICU database. The endotracheal tubes (ETTs), central venous catheters (CVCs), and nasogastric tubes (NGTs) were classified into "Normal," "Abnormal," or "Borderline" positions by DL models with and without rule-based modification. Their performance was evaluated using an experienced radiologist as the standard reference. Results: The algorithm showed decreased performance on the local ICU dataset, compared to that of the external dataset, decreasing from the Area Under the Curve of Receiver (AUC) of 0.967 (95 % CI 0.965-0.973) to the AUC of 0.70 (95 % CI 0.68-0.77). Significant improvement in the ETT classification task was observed after modifications were made to the model to allow the use of the spatial relationship between line tips and reference anatomy with the improvement of the AUC, increasing from 0.71 (95 % CI 0.70 - 0.75) to 0.86 (95 % CI 0.83 - 0.94). Conclusions: The externally trained model exhibited limited generalizability on the local ICU dataset. Therefore, evaluating the performance of externally trained AI before integrating it into critical care routine is crucial. Rule-based algorithm may be used in combination with DL to improve results.

A Comparison of an Australian Observational Longitudinal Alzheimer's Disease Cohort to Community-Based Australian Data.

Background: Observational Alzheimer's disease (AD) cohorts including the Australian, Biomarkers, Imaging and Lifestyle (AIBL) Study have enhanced our understanding of AD. The generalizability of findings from AIBL to the general population has yet to be studied. Objective: We aimed to compare characteristics of people with AD dementia in AIBL to 1) the general population of older Australians using pharmacological treatment for AD dementia, and to 2) the general population of older Australians who self-reported a diagnosis of dementia. Methods: Descriptive study comparing people aged 65 years of over (1) in AIBL that had a diagnosis of AD dementia, (2) dispensed with pharmacological treatment for AD in Australia in 2021 linked to the Australian census in 2021 (refer to as PBS/census), (3) self-reported a diagnosis of dementia in the 2021 Australian census (refer to as dementia/census). Baseline characteristics included age, sex, highest education attainment, primary language, and medical co-morbidities. Results: Participants in AIBL were younger, had more years of education, and had a lower culturally and linguistically diverse (CALD) population compared to the PBS/census cohort and dementia/census cohort (mean age±standard deviation - AIBL 79±7 years, PBS/census 81±7, p < 0.001, dementia/census 83±8, p < 0.001; greater than 12 years of education AIBL 40%, PBS/census 35%, p = 0.020, dementia/census 29%, p < 0.001; CALD - AIBL 3%, PBS/census 20%, p < 0.001, dementia/census 22%, p < 0.001). Conclusions: Our findings suggest that care should be taken regarding the generalizability of AIBL in CALD populations and the interpretation of results on the natural history of AD.

Customizing Bayesian multivariate generalizability theory to mixed-format tests.

Mixed-format tests, which typically include dichotomous items and polytomously scored tasks, are employed to assess a wider range of knowledge and skills. Recent behavioral and educational studies have highlighted their practical importance and methodological developments, particularly within the context of multivariate generalizability theory. However, the diverse response types and complex designs of these tests pose significant analytical challenges when modeling data simultaneously. Current methods often struggle to yield reliable results, either due to the inappropriate treatment of different types of response data separately or the imposition of identical covariates across various response types. Moreover, there are few software packages or programs that offer customized solutions for modeling mixed-format tests, addressing these limitations. This tutorial provides a detailed example of using a Bayesian approach to model data collected from a mixed-format test, comprising multiple-choice questions and free-response tasks. The modeling was conducted using the Stan software within the R programming system, with Stan codes tailored to the structure of the test design, following the principles of multivariate generalizability theory. By further examining the effects of prior distributions in this example, this study demonstrates how the adaptability of Bayesian models to diverse test formats, coupled with their potential for nuanced analysis, can significantly advance the field of psychometric modeling.

The Factor Structure and Generalizability of the Iranian Socioeconomic Status (SES) Questionnaire Administered in a Nationally Divergent Population.

Background: Measuring socioeconomic status (SES) as an independent variable is challenging, especially in epidemiological and social studies. This issue is more critical in large-scale studies on the national level. The present study aimed to extensively evaluate the validity and reliability of the Iranian SES questionnaire. Methods: This psychometric, cross-sectional study was conducted on 3000 households, selected via random cluster sampling from various areas in East Azerbaijan province and Tehran, Iran. Moreover, 250 students from Tabriz University of Medical Sciences were selected as interviewers to collect data from 40 districts in Iran. The construct validity and internal consistency of the SES questionnaire were assessed using exploratory and confirmatory factor analyses and the Cronbach's alpha. Data analysis was performed in SPSS and AMOS. Results: The complete Iranian version of the SES questionnaire consists of 5 factors. The Cronbach's alpha was calculated to be 0.79, 0.94, 0.66, 0.69, and 0.48 for the occupation, self-evaluation of economic capacity, house and furniture, wealth, and health expenditure, respectively. In addition, the confirmatory factor analysis results indicated the data's compatibility with the 5-factor model (comparative fit index = 0.96; goodness of fit index = 0.95; incremental fit index = 0.96; root mean square error of approximation = 0.05). Conclusion: According to the results, the confirmed validity and reliability of the tool indicated that the Iranian version of the SES questionnaire could be utilized with the same structure on an extensive level and could be applicable for measuring the SES in a broader range of populations.

Eligibility of Asian and European registry patients for phase III trials in heart failure with reduced ejection fraction.

AIMS: Traditional approaches to designing clinical trials for heart failure (HF) have historically relied on expertise and past practices. However, the evolving landscape of healthcare, marked by the advent of novel data science applications and increased data availability, offers a compelling opportunity to transition towards a data-driven paradigm in trial design. This research aims to evaluate the scope and determinants of disparities between clinical trials and registries by leveraging natural language processing for the analysis of trial eligibility criteria. The findings contribute to the establishment of a robust design framework for guiding future HF trials. METHODS AND RESULTS: Interventional phase III trials registered for HF on ClinicalTrials.gov as of the end of 2021 were identified. Natural language processing was used to extract and structure the eligibility criteria for quantitative analysis. The most common criteria for HF with reduced ejection fraction (HFrEF) were applied to estimate patient eligibility as a proportion of registry patients in the ASIAN-HF (N = 4868) and BIOSTAT-CHF registries (N = 2545). Of the 375 phase III trials for HF, 163 HFrEF trials were identified. In these trials, the most frequently encountered inclusion criteria were New York Heart Association (NYHA) functional class (69%), worsening HF (23%), and natriuretic peptides (18%), whereas the most frequent comorbidity-based exclusion criteria were acute coronary syndrome (64%), renal disease (55%), and valvular heart disease (47%). On average, 20% of registry patients were eligible for HFrEF trials. Eligibility distributions did not differ (P = 0.18) between Asian [median eligibility 0.20, interquartile range (IQR) 0.08-0.43] and European registry populations (median 0.17, IQR 0.06-0.39). With time, HFrEF trials became more restrictive, where patient eligibility declined from 0.40 in 1985-2005 to 0.19 in 2016-2022 (P = 0.03). When frequency among trials is taken into consideration, the eligibility criteria that were most restrictive were prior myocardial infarction, NYHA class, age, and prior HF hospitalization. CONCLUSIONS: Based on 14 trial criteria, only one-fifth of registry patients were eligible for phase III HFrEF trials. Overall eligibility rates did not differ between the Asian and European patient cohorts.

Reliability of the Test of Gross Motor Development Third Edition Among Children with Developmental Coordination Disorder.

AIM: The Test of Gross Motor Development Third Edition (TGMD-3) is used to assess the development of fundamental movement skills in children from 3 to 10 years old. This study aimed to evaluate the intra-rater, inter-rater, and test-retest reliability and to determine the minimal detectable change (MDC) value of the TGMD-3 in children with developmental coordination disorder (DCD). METHODS: The TGMD-3 was administered to 20 children with DCD. The child's fundamental movement skills were recorded using a digital video camera. Reliability was assessed at two occasions by three raters using the generalizability theory. RESULTS: The TGMD-3 demonstrates good inter-rater reliability for the locomotor skills subscale, the ball skills subscale, and the total score (φ = 0.77 - 0.91), while the intra-rater reliability was even higher (φ = 0.94 - 0.97). Test-retest reliability was also shown to be good (φ = 0.79-0.93). The MDC95 was determined to be 10 points. CONCLUSION: This study provides evidence that the TGMD-3 is a reliable test when used to evaluate fundamental movement skills in children with DCD and suggests that an increase of 10 points represents a significant change in the motor function of a child with DCD.

Integrative nomogram model based on anoikis-related genes enhances prognostic evaluation in colorectal cancer.

Background: Revealing the role of anoikis resistance plays in CRC is significant for CRC diagnosis and treatment. This study integrated the CRC anoikis-related key genes (CRC-AKGs) and established a novel model for improving the efficiency and accuracy of the prognostic evaluation of CRC. Methods: CRC-ARGs were screened out by performing differential expression and univariate Cox analysis. CRC-AKGs were obtained through the LASSO machine learning algorithm and the LASSO Risk-Score was constructed to build a nomogram clinical prediction model combined with the clinical predictors. In parallel, this work developed a web-based dynamic nomogram to facilitate the generalization and practical application of our model. Results: We identified 10 CRC-AKGs and a risk-related prognostic Risk-Score was calculated. Multivariate COX regression analysis indicated that the Risk-Score, TNM stage, and age were independent risk factors that significantly associated with the CRC prognosis(p < 0.05). A prognostic model was built to predict the outcome with satisfied accuracy (3-year AUC = 0.815) for CRC individuals. The web interactive nomogram (https://yuexiaozhang.shinyapps.io/anoikisCRC/) showed strong generalizability of our model. In parallel, a substantial correlation between tumor microenvironment and Risk-Score was discovered in the present work. Conclusion: This study reveals the potential role of anoikis in CRC and sets new insights into clinical decision-making in colorectal cancer based on both clinical and sequencing data. Also, the interactive tool provides researchers with a user-friendly interface to input relevant clinical variables and obtain personalized risk predictions or prognostic assessments based on our established model.

Heterogeneity in effect size estimates.

A typical empirical study involves choosing a sample, a research design, and an analysis path. Variation in such choices across studies leads to heterogeneity in results that introduce an additional layer of uncertainty, limiting the generalizability of published scientific findings. We provide a framework for studying heterogeneity in the social sciences and divide heterogeneity into population, design, and analytical heterogeneity. Our framework suggests that after accounting for heterogeneity, the probability that the tested hypothesis is true for the average population, design, and analysis path can be much lower than implied by nominal error rates of statistically significant individual studies. We estimate each type's heterogeneity from 70 multilab replication studies, 11 prospective meta-analyses of studies employing different experimental designs, and 5 multianalyst studies. In our data, population heterogeneity tends to be relatively small, whereas design and analytical heterogeneity are large. Our results should, however, be interpreted cautiously due to the limited number of studies and the large uncertainty in the heterogeneity estimates. We discuss several ways to parse and account for heterogeneity in the context of different methodologies.

Uncertainty-aware multiple-instance learning for reliable classification: Application to optical coherence tomography.

Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability. We aimed to enhance the reliability of deep learning classification models using a novel method called Uncertainty-Based Instance eXclusion (UBIX). UBIX is an inference-time module that can be employed in multiple-instance learning (MIL) settings. MIL is a paradigm in which instances (generally crops or slices) of a bag (generally an image) contribute towards a bag-level output. Instead of assuming equal contribution of all instances to the bag-level output, UBIX detects instances corrupted due to local artifacts on-the-fly using uncertainty estimation, reducing or fully ignoring their contributions before MIL pooling. In our experiments, instances are 2D slices and bags are volumetric images, but alternative definitions are also possible. Although UBIX is generally applicable to diverse classification tasks, we focused on the staging of age-related macular degeneration in optical coherence tomography. Our models were trained on data from a single scanner and tested on external datasets from different vendors, which included vendor-specific artifacts. UBIX showed reliable behavior, with a slight decrease in performance (a decrease of the quadratic weighted kappa (κw) from 0.861 to 0.708), when applied to images from different vendors containing artifacts; while a state-of-the-art 3D neural network without UBIX suffered from a significant detriment of performance (κw from 0.852 to 0.084) on the same test set. We showed that instances with unseen artifacts can be identified with OOD detection. UBIX can reduce their contribution to the bag-level predictions, improving reliability without retraining on new data. This potentially increases the applicability of artificial intelligence models to data from other scanners than the ones for which they were developed. The source code for UBIX, including trained model weights, is publicly available through https://github.com/qurAI-amsterdam/ubix-for-reliable-classification.