Systemic Arterial Stiffness and Choroidal Microvascular Insufficiency on the Structural Progression of Normal Tension Glaucoma.

PURPOSE: To identify the role of systemic arterial stiffness and choroidal microvascular insufficiency on structural progression of normal-tension glaucoma (NTG). DESIGN: Retrospective cohort study. METHODS: A total of 107 early NTG eyes of 88 patients, who underwent pulse wave velocity (PWV) measurements and optical coherence tomography (OCT) angiography (OCT-A) at baseline, were categorized depending on the presence of peripapillary choroidal microvasculature dropout (MvD) and PWV. Differences in glaucomatous progression were analyzed. Structural progression rates were determined using the trend-based analysis of cirrus OCT. RESULTS: Thirty-two eyes displayed choroidal MvD (62.7 (95% CI 58.4-67.0) years old, 53.6% males), and 70 eyes did not show any MvD (59.9 (95% CI 57.1-62.6) years old, 53.3% males) at baseline. Patients were followed for 48.4 (95% CI 40.0-56.8) months. When they were further divided based on PWV (high PWV≥1400cm/sec), those with choroidal MvD and high PWV showed significantly faster thinning in macular ganglion cell-inner plexiform layer (GCIPL; P=0.023). In comparison to those with low PWV and no MvD, eyes with high PWV and MvD in the peripapillary area were likely to show fast structural progression (≤-1.2 µm/year) in the macular GCIPL by odds of 6.019 (95% CI 1.619-38.531, P=0.025). CONCLUSIONS: In NTG eyes, GCIPL thinning was faster when choroidal MvD and high systemic arterial stiffness were present. The simultaneous presence of regional and systemic vascular insufficiency may be associated with rapid glaucoma structural progression in eyes with low baseline intraocular pressure.

Using Monte Carlo Simulation to Forecast the Scientific Utility of Psychological App Studies: A Tutorial.

Mobile applications offer a wide range of opportunities for psychological data collection, such as increased ecological validity and greater acceptance by participants compared to traditional laboratory studies. However, app-based psychological data also pose data-analytic challenges because of the complexities introduced by missingness and interdependence of observations. Consequently, researchers must weigh the advantages and disadvantages of app-based data collection to decide on the scientific utility of their proposed app study. For instance, some studies might only be worthwhile if they provide adequate statistical power. However, the complexity of app data forestalls the use of simple analytic formulas to estimate properties such as power. In this paper, we demonstrate how Monte Carlo simulations can be used to investigate the impact of app usage behavior on the utility of app-based psychological data. We introduce a set of questions to guide simulation implementation and showcase how we answered them for the simulation in the context of the guessing game app Who Knows (Rau et al., 2023). Finally, we give a brief overview of the simulation results and the conclusions we have drawn from them for real-world data generation. Our results can serve as an example of how to use a simulation approach for planning real-world app-based data collection.

The Involvement of Academic and Emotional Support for Sustainable Use of MOOCs.

MOOCs, the Massive Open Online Courses, are online educational courses that offer open access to a large number of participants globally. However, online engagement during MOOC learning remains a problem, as reflected in relatively high dropout rates. This paper involves academic and emotional support, aiming to explore whether they contribute to users' sustainable use of the MOOC platform. A total of 410 college students learning English as a foreign language (EFL) and with MOOC learning experience participated in this study. Employing the structural equation modeling (SEM) techniques, we examined the relationships among five factors in the EFL MOOC learning context: academic support (AS), emotional support (ES), perceived usefulness (PU), perceived ease of use (PEoU), and platform reputation (PR). The results indicate that academic support influences learners' perceptions of the usefulness and ease of use of the MOOC platform, as well as enhancing learners' feelings of being emotionally supported. Simultaneously, platform reputation plays a crucial role in influencing learners' perceptions of MOOC platforms. However, results suggest that emotional support does not have a statistically significant impact on the perceived usefulness and perceived ease of use of the platform in EFL MOOC learning contexts.

A Bayesian convolutional neural network-based generalized linear model.

Convolutional neural networks (CNNs) provide flexible function approximations for a wide variety of applications when the input variables are in the form of images or spatial data. Although CNNs often outperform traditional statistical models in prediction accuracy, statistical inference, such as estimating the effects of covariates and quantifying the prediction uncertainty, is not trivial due to the highly complicated model structure and overparameterization. To address this challenge, we propose a new Bayesian approach by embedding CNNs within the generalized linear models (GLMs) framework. We use extracted nodes from the last hidden layer of CNN with Monte Carlo (MC) dropout as informative covariates in GLM. This improves accuracy in prediction and regression coefficient inference, allowing for the interpretation of coefficients and uncertainty quantification. By fitting ensemble GLMs across multiple realizations from MC dropout, we can account for uncertainties in extracting the features. We apply our methods to biological and epidemiological problems, which have both high-dimensional correlated inputs and vector covariates. Specifically, we consider malaria incidence data, brain tumor image data, and fMRI data. By extracting information from correlated inputs, the proposed method can provide an interpretable Bayesian analysis. The algorithm can be broadly applicable to image regressions or correlated data analysis by enabling accurate Bayesian inference quickly.

Clusters Based on Within-Treatment Symptom Trajectories as Predictors of Dropout in Treatment for Posttraumatic Stress Disorder and Substance Use Disorder.

Objective: Dropout rates are high in treatments for co-occurring posttraumatic stress disorder (PTSD) and substance use disorders (SUDs). We examined dropout predictors in PTSD-SUD treatment. Methods: Participants were 183 veterans receiving integrated or phased motivational enhancement therapy and prolonged exposure. Using survival models, we examined demographics and symptom trajectories as dropout predictors. Using latent trajectory analysis, we incorporated clusters based on symptom trajectories to improve dropout prediction. Results: Hispanic ethnicity (integrated arm), Black or African American race (phased arm), and younger age (phased arm) predicted dropout. Clusters based on PTSD and substance use trajectories improved dropout prediction. In integrated treatment, participants with consistently-high use and low-and-improving use had the highest dropout. In phased treatment, participants with the highest and lowest PTSD symptoms had lower dropout; participants with the lowest substance use had higher dropout. Conclusions: Identifying within-treatment symptom trajectories associated with dropout can help clinicians intervene to maximize outcomes. ClinicalTrials.gov Identifier: NCT01211106.

Predictive modelling of student dropout risk: Practical insights from a South Korean distance university.

Distance education supports lifelong learning and empowers individuals in rapidly changing societal conditions, yet it encounters high dropout rates due to a range of individual and societal obstacles. This study addresses the challenge of creating a practical prediction model by analyzing extensive real-world time-point data from a well-established online university in Seoul. Covering 144,540 instances from 2018 to 2022, the study integrates diverse datasets to compare the accuracy of models based on longitudinal, semester-wise, and gender-specific datasets. The demographic, academic, and online metrics identified significant dropout indicators, including age (particularly when binned), residential area, specific occupations, GPA, and LMS log metrics, using a stepwise backward elimination process. The study revealed that, despite societal changes, recent data from the last four semesters can be effectively used for stable prediction training. Gender-based analysis showed different factors influencing dropout risk for males and females. The Light Gradient Boosting Machine (LGBM) algorithm excelled in prediction accuracy, with the ROC-AUC metric affirming its superiority. However, logistic regression also showed its competitive performance and offered in-depth interpretation. In South Korea's distinct educational setting, merging advanced algorithms like LGBM with the interpretive strength of logistic regression is key for effective student support strategies.

Achieving optimal trade-off for student dropout prediction with multi-objective reinforcement learning.

Student dropout prediction (SDP) in educational research has gained prominence for its role in analyzing student learning behaviors through time series models. Traditional methods often focus singularly on either prediction accuracy or earliness, leading to sub-optimal interventions for at-risk students. This issue underlines the necessity for methods that effectively manage the trade-off between accuracy and earliness. Recognizing the limitations of existing methods, this study introduces a novel approach leveraging multi-objective reinforcement learning (MORL) to optimize the trade-off between prediction accuracy and earliness in SDP tasks. By framing SDP as a partial sequence classification problem, we model it through a multiple-objective Markov decision process (MOMDP), incorporating a vectorized reward function that maintains the distinctiveness of each objective, thereby preventing information loss and enabling more nuanced optimization strategies. Furthermore, we introduce an advanced envelope Q-learning technique to foster a comprehensive exploration of the solution space, aiming to identify Pareto-optimal strategies that accommodate a broader spectrum of preferences. The efficacy of our model has been rigorously validated through comprehensive evaluations on real-world MOOC datasets. These evaluations have demonstrated our model's superiority, outperforming existing methods in achieving optimal trade-off between accuracy and earliness, thus marking a significant advancement in the field of SDP.

Spatially-informed insights into early marriage and school dropout: An advanced bivariate binary multilevel model for understanding Ethiopia's context.

The phenomenon of school dropout, which entails the failure to meet the minimum educational requirements, and early marriage, which involves the marital union of girls prior to attaining 18 years of age, constitute crucial issues in Ethiopia. This research endeavor sought to identify the determinants of these two outcomes. A weighted sample of 3091 girls who had experienced early marriage and school dropout was drawn from the 2016 Ethiopian Demographic and Health Survey (EDHS) dataset and analyzed utilizing bivariate binary multilevel models featuring spatial effects. The prevalence rates of early marriage and school dropout were 62.9 % and 75.4 %, respectively. We observed non-uniform spatial distributions of early marriage and school dropout across Ethiopia. The odds ratio of the association between early marriage and school dropout was 1.39, indicating a significant interdependence of these two outcomes. The probability of early marriage and school dropout was estimated to be 1.63 and 1.18 times higher, respectively, for girls hailing from rural areas and 1.70 and 1.23 times higher, respectively, for those classified in the poorest wealth index, as compared to their counterparts. Therefore, stakeholders and policymakers must prioritize hotspots, socio-economic, and demographic factors to achieve a meaningful reduction in the incidence of early marriage and school dropout.

Continuous Risk Score Predicts Waitlist & Post-Transplant Outcomes in Hepatocellular Carcinoma Despite Exception Changes.

BACKGROUND AND AIMS: Continuous risk stratification of candidates and urgency-based prioritization have been utilized for liver transplantation (LT) in non-hepatocellular carcinoma (HCC) patients in the United States. Instead, for HCC patients, a dichotomous criterion with exception points is still used. This study evaluated the utility of the hazard associated with LT for HCC (HALT-HCC), an oncological continuous risk score, to stratify waitlist dropout and post-LT outcomes. METHODS: A competing risk model was developed and validated using the UNOS database (2012-2021) through multiple policy changes. The primary outcome was to assess the discrimination ability of waitlist dropouts and LT outcomes. The study focused on the HALT-HCC score, compared to other HCC risk scores. RESULTS: Among 23,858 candidates, 14,646 (59.9%) underwent LT and 5,196 (21.8%) dropped out of the waitlist. Higher HALT-HCC scores correlated with increased dropout incidence and lower predicted five-year overall survival after LT. HALT-HCC demonstrated the highest AUC values for predicting dropout at various intervals post-listing (0.68 at six months, 0.66 at one year), with excellent calibration (R2=0.95 at six months, 0.88 at one year). Its accuracy remained stable across policy periods and locoregional therapy applications. CONCLUSIONS: This study highlights the predictive capability of the continuous oncological risk score to forecast waitlist dropout and post-LT outcomes in HCC patients, independent of policy changes. The study advocates integrating continuous scoring systems like HALT-HCC in liver allocation decisions, balancing urgency, organ utility, and survival benefit.

Machine learning predicts upper secondary education dropout as early as the end of primary school.

Education plays a pivotal role in alleviating poverty, driving economic growth, and empowering individuals, thereby significantly influencing societal and personal development. However, the persistent issue of school dropout poses a significant challenge, with its effects extending beyond the individual. While previous research has employed machine learning for dropout classification, these studies often suffer from a short-term focus, relying on data collected only a few years into the study period. This study expanded the modeling horizon by utilizing a 13-year longitudinal dataset, encompassing data from kindergarten to Grade 9. Our methodology incorporated a comprehensive range of parameters, including students' academic and cognitive skills, motivation, behavior, well-being, and officially recorded dropout data. The machine learning models developed in this study demonstrated notable classification ability, achieving a mean area under the curve (AUC) of 0.61 with data up to Grade 6 and an improved AUC of 0.65 with data up to Grade 9. Further data collection and independent correlational and causal analyses are crucial. In future iterations, such models may have the potential to proactively support educators' processes and existing protocols for identifying at-risk students, thereby potentially aiding in the reinvention of student retention and success strategies and ultimately contributing to improved educational outcomes.

Predictors of dropout, time spent on the program and client satisfaction in an internet-based, telephone-assisted CBT anxiety program among elementary school children in a population-based sample.

Increasing evidence has shown that childhood anxiety can be effectively treated by Internet-based cognitive behavioral therapy (ICBT). Being able to predict why participants decide to drop out of such programs enables scarce resources to be used appropriately. The aim of this study was to report dropout predictors for a population-based ICBT intervention aimed at children with anxiety, together with the time they and their parents spent on the program and client satisfaction rates. The study focused on 234 Finnish children aged 10-13 who received an ICBT intervention, with telephone support, for anxiety symptoms, as a part of a randomized control trial. Their parents also had access to Internet-based material and participated in the weekly telephone calls with the coach. Possible drop out factors were explored and these included various family demographics, child and parent psychopathology and therapeutic alliance. Just under a fifth (23.9%) of the children dropped out of the intervention. The risk was higher if the child did not fulfill the criteria for any anxiety diagnosis or reported a poorer therapeutic alliance. Family demographics and the COVID-19 pandemic did not increase the risk. The families spent an average of 127 min on the webpage each week and an average of 32 min on the phone calls. The overall satisfaction with the program was 87% for the children and 95% for the parents. Both the children and the parents found the telephone calls helpful. These findings are important in clinical practice when assessing a family's eligibility for ICBT.

The key reasons for dropout in Slovenian music schools - a qualitative study.

Music education often struggles to sustain students' long-term commitment, with many perceiving lessons as frustrating or unengaging, leading to discontinuation. To address this gap, our study aimed to elucidate the primary reasons for dropout from the perspectives of various stakeholders, including students, parents, teachers, and principals. Drawing upon the self-determination theory, our research comprehensively investigated external and internal factors contributing to dropout. Among external factors, competing extracurricular commitments, music theory and solfége lessons, and teacher's approach emerge as the most prominent. Among internal factors, our findings highlighted the critical role of autonomy, competency, and relatedness in shaping students' decisions to continue or discontinue music education. Inadequate teacher-student relationships, limited peer interactions, and uninspiring classroom atmospheres significantly impacted dropout. Moreover, challenges in the music school curriculum, such as difficulties with music theory and solfège, resource limitations, and excessive workloads, emerged as prominent barriers to student engagement. By addressing these multifaceted issues, our study underscores the importance of fostering supportive environments that cater to individual needs and interests, ultimately enhancing the overall music education experience and reducing dropout rates. This research represents the first systematic empirical study in Slovenian music education, laying the groundwork for future quantitative investigations to advance education practices in Slovenia.

The magnitude of gender-based violence, health consequences, and associated factors among women living in post-war woredas of North Shewa zone, Amhara, Ethiopia, 2022.

Background: Gender-based violence (GBV) is a pervasive global public health concern and a violation of human rights, particularly pronounced in conflict settings where it is often used as a tool of warfare to instill fear and control populations. Objective: Assessment of Magnitude, Associated Factors, and Health Consequences of GBV among women living in war-affected woredas of North Shewa zone, Ethiopia, 2022. Methods: A community-based cross-sectional study was conducted, involving 845 randomly selected women living in conflict zones. Data on GBV experiences over the previous 3 months were collected through interviewer-administered questionnaires developed from literature review. The collected data underwent validation, entry into EPI data, and analysis using SPSS. Findings are summarized using descriptive statistics, AOR and 95% confidence interval. Result: The magnitude of GBV in this study was (490, 58.0%) where, (466, 55.0%) psychological violence, (254, 30.1%) physical violence, and (135, 16.0%) reported sexual violence. A majority of the physical violence, (161, 63.4%), occurred during conflict period, with (143, 56.3%) of these cases involving armed forces, and (161, 63.4%) women experiencing physical violence in their homes. Urban Residence AOR = 2.65, CI, (1.82-3.89), Educational status of Secondary education AOR = 0.33, CI, (0.19-0.57, and ≥College AOR = 0.17, CI, (0.09-0.35), Occupation of Housewife AOR = 1.88, CI, (1.20-2.94), Private employee AOR = 6.95, CI, (3.70-13.04), Gov't employee AOR = 5.80, CI, (2.92-11.50), and others (Students) AOR = 3.46, CI, (1.98-6.01), Ever had sexual intercourse AOR = 0.46, CI, (0.25-0.83), Have heard about SRH AOR = 0.59, CI, (0.40-0.89), Have had previous GBV exposure AOR = 0.24, CI, (0.15-0.38), having a previous history of sexual violence AOR = 0.30, CI, (0.16-0.57), and Number of sexual partner AOR = 1.84, CI, (1.13-2.99) were identified to be associated factors of GBV in our study area. The most commonly reported consequences of GBV were Anxiety, depression, physical injuries, self-blame, women had school dropout, and abortion. Conclusion: The study reveals a higher prevalence of GBV, resulting in profound physical, social, mental, and reproductive health challenges for survivors. To address this, multi-sectoral cooperation is advised to enhance women's empowerment, access to information, and psycho-social support in affected areas. Furthermore, national policymakers are urged to implement preventive measures during conflict and establish legal mechanisms to ensure accountability for perpetrators.

Dropout in cognitive behavioral treatment in adults living with overweight and obesity: a systematic review.

Obesity is a chronic, complex, and multifactorial disease resulting from the interaction of genetic, environmental, and behavioral factors. It is characterized by excessive fat accumulation in adipose tissue, which damages health and deteriorates the quality of life. Although dietary treatment can significantly improve health, high attrition is a common problem in weight loss interventions with serious consequences for weight loss management and frustration. The strategy used to improve compliance has been combining dietary prescriptions and recommendations for physical activity with cognitive behavioral treatment (CBT) for weight management. This systematic review determined the dropout rate and predictive factors associated with dropout from CBT for adults with overweight and obesity. The data from the 37 articles selected shows an overall dropout rate between 5 and 62%. The predictive factors associated with attrition can be distinguished by demographics (younger age, educational status, unemployed status, and ethnicity) and psychological variables (greater expected 1-year Body Mass Index loss, previous weight loss attempts, perceiving more stress with dieting, weight and shape concerns, body image dissatisfaction, higher stress, anxiety, and depression). Common reasons for dropping out were objective (i.e., long-term sickness, acute illness, and pregnancy), logistical, poor job conditions or job difficulties, low level of organization, dissatisfaction with the initial results, lack of motivation, and lack of adherence. According to the Mixed Methods Appraisal quality analysis, 13.5% of articles were classified as five stars, and none received the lowest quality grade (1 star). The majority of articles were classified as 4 stars (46%). At least 50% of the selected articles exhibited a high risk of bias. The domain characterized by a higher level of bias was that of randomization, with more than 60% of the articles having a high risk of bias. The high risk of bias in these articles can probably depend on the type of study design, which, in most cases, was observational and non-randomized. These findings demonstrate that CBT could be a promising approach for obesity treatment, achieving, in most cases, lower dropout rates than other non-behavioral interventions. However, more studies should be conducted to compare obesity treatment strategies, as there is heterogeneity in the dropout assessment and the population studied. Ultimately, gaining a deeper understanding of the comparative effectiveness of these treatment strategies is of great value to patients, clinicians, and healthcare policymakers. Systematic review registration: PROSPERO 2022 CRD42022369995 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022369995.

Determinants of second-dose measles vaccination dropout in Ethiopia: A community-based matched case-control study.

Background: Measles vaccination is the most important public health intervention and a cost-effective strategy to reduce morbidity and mortality in under-five children. Although Ethiopia's government developed a measles elimination strategic plan by 2020, the full coverage of immunization was 43 %. Therefore, this study aimed to identify determinants of second-dose measles vaccination (MCV2) dropout among children aged 24-35 months in East Bale Zone, Ethiopia. Method: A community-based matched case-control study was conducted among 351 children (117 cases and 234 controls). Children who received the first dose of measles vaccine but did not receive the second dose were cases, and children who received both doses of measles vaccine were control. The matches were based on age and residence. The data were collected using a structured questionnaire, entered into Epi Data 3.1, cleaned, exported, and analyzed using Stata version 16.1. A multivariable conditional logistic regression analysis was performed. Variables with a P value of <0.05 were considered significant determinants of the dependent variable at the 95 % confidence level. Results: Mothers who were unable to read and write (mAOR: 4.0; 95 % CI: 1.59-10.2), did not receive counseling (mAOR: 3.19; 95 % CI: 1.62-6.27), spent ≥30 min to reach health facilities (mAOR; 2.76, 95 % CI: 1.25-6.1), and did not attend postnatal care (mAOR; 3.46, 95 % CI: 1.58-7.57) were significantly and positively associated with second-dose measles vaccination dropout. In addition, mothers who had poor knowledge of second-dose measles vaccination (mAOR; 3.20, 95 % CI: 1.50-6.70) and waited more than an hour for measles vaccination at health facilities (mAOR; 2.61, 95 % CI: 1.0-6.20) were significantly more likely to experience second-dose measles vaccine dropout. Conclusions: The key factors associated with second-dose measles vaccination dropout are maternal illiteracy, lack of PNC, inadequate maternal knowledge and poor counseling about MCV2 vaccination, long distances travel to healthcare facilities and extended waiting times at vaccination providing sites. Health extension workers emphasize strengthening home visit programs in catchment households to improve mothers' awareness of measles vaccination.

Dataset size versus homogeneity: A machine learning study on pooling intervention data in e-mental health dropout predictions.

Objective: This study proposes a way of increasing dataset sizes for machine learning tasks in Internet-based Cognitive Behavioral Therapy through pooling interventions. To this end, it (1) examines similarities in user behavior and symptom data among online interventions for patients with depression, social anxiety, and panic disorder and (2) explores whether these similarities suffice to allow for pooling the data together, resulting in more training data when prediction intervention dropout. Methods: A total of 6418 routine care patients from the Internet Psychiatry in Stockholm are analyzed using (1) clustering and (2) dropout prediction models. For the latter, prediction models trained on each individual intervention's data are compared to those trained on all three interventions pooled into one dataset. To investigate if results vary with dataset size, the prediction is repeated using small and medium dataset sizes. Results: The clustering analysis identified three distinct groups that are almost equally spread across interventions and are instead characterized by different activity levels. In eight out of nine settings investigated, pooling the data improves prediction results compared to models trained on a single intervention dataset. It is further confirmed that models trained on small datasets are more likely to overestimate prediction results. Conclusion: The study reveals similar patterns of patients with depression, social anxiety, and panic disorder regarding online activity and intervention dropout. As such, this work offers pooling different interventions' data as a possible approach to counter the problem of small dataset sizes in psychological research.

Regulatory Issues in Electronic Health Records for Adolescent HIV Research: Strategies and Lessons Learned.

BACKGROUND: Electronic health records (EHRs) are a cost-effective approach to provide the necessary foundations for clinical trial research. The ability to use EHRs in real-world clinical settings allows for pragmatic approaches to intervention studies with the emerging adult HIV population within these settings; however, the regulatory components related to the use of EHR data in multisite clinical trials poses unique challenges that researchers may find themselves unprepared to address, which may result in delays in study implementation and adversely impact study timelines, and risk noncompliance with established guidance. OBJECTIVE: As part of the larger Adolescent Trials Network (ATN) for HIV/AIDS Interventions Protocol 162b (ATN 162b) study that evaluated clinical-level outcomes of an intervention including HIV treatment and pre-exposure prophylaxis services to improve retention within the emerging adult HIV population, the objective of this study is to highlight the regulatory process and challenges in the implementation of a multisite pragmatic trial using EHRs to assist future researchers conducting similar studies in navigating the often time-consuming regulatory process and ensure compliance with adherence to study timelines and compliance with institutional and sponsor guidelines. METHODS: Eight sites were engaged in research activities, with 4 sites selected from participant recruitment venues as part of the ATN, who participated in the intervention and data extraction activities, and an additional 4 sites were engaged in data management and analysis. The ATN 162b protocol team worked with site personnel to establish the necessary regulatory infrastructure to collect EHR data to evaluate retention in care and viral suppression, as well as para-data on the intervention component to assess the feasibility and acceptability of the mobile health intervention. Methods to develop this infrastructure included site-specific training activities and the development of both institutional reliance and data use agreements. RESULTS: Due to variations in site-specific activities, and the associated regulatory implications, the study team used a phased approach with the data extraction sites as phase 1 and intervention sites as phase 2. This phased approach was intended to address the unique regulatory needs of all participating sites to ensure that all sites were properly onboarded and all regulatory components were in place. Across all sites, the regulatory process spanned 6 months for the 4 data extraction and intervention sites, and up to 10 months for the data management and analysis sites. CONCLUSIONS: The process for engaging in multisite clinical trial studies using EHR data is a multistep, collaborative effort that requires proper advanced planning from the proposal stage to adequately implement the necessary training and infrastructure. Planning, training, and understanding the various regulatory aspects, including the necessity of data use agreements, reliance agreements, external institutional review board review, and engagement with clinical sites, are foremost considerations to ensure successful implementation and adherence to pragmatic trial timelines and outcomes.

Device-Related Skin Reactions Increase Emotional Burden in Youths With Type 1 Diabetes and Their Parents.

BACKGROUND: Skin reactions due to technological devices pose a significant concern in the management of type 1 diabetes (T1D). This multicentric, comparative cross-sectional study aimed to assess the psychological impact of device-related skin issues on youths with T1D and their parents. METHODS: Participants with skin reactions were matched in a 1:1 ratio with a control group. Diabetes-related emotional distress was evaluated using the Problem Areas in Diabetes-Teen version (PAID-T) for participants aged 11 to 19 years and the Problem Areas in Diabetes-Parent Revised version (PAID-PR) completed by parents. In addition, glucose control was assessed through glycated hemoglobin (HbA1c) values and continuous glucose monitoring (CGM) metrics. RESULTS: A total of 102 children and adolescents were consecutively recruited. Adolescents with skin issues had higher PAID-T scores compared to those without (79.6 ± 21.1 vs 62 ± 16.8; P = .004). Parents of youths with skin reactions also reported higher PAID-PR scores than the control group (34.0 ± 11.0 vs 26.9 ± 12.3; P = .015). No differences were observed in HbA1c levels (6.9 ± 0.8% vs 6.8 ± 0.8%, P = .555) or CGM glucose metrics between the two groups. Remarkably, 25.5% were forced to discontinue insulin pumps and/or glucose sensors (21.5% and 5.9%, respectively). CONCLUSIONS: Our study highlighted the increased emotional burden experienced by youths with T1D and their parents due to device-related skin reactions, emphasizing the need for further research and interventions in this crucial aspect of diabetes management.

Exploring factors of adherence to weight loss interventions in population with overweight/obesity: an umbrella review.

Adherence is key for achieving the optimal benefits from a weight loss intervention. Despite the number of studies on factors that promote adherence, their findings suggest inconsistent and fragmented evidence. The aim of this study was to review the existing factors of adherence to weight loss interventions and to find factors that facilitate the design of effective intervention programs. Six databases were searched for this umbrella review; after the screening process, 21 studies were included. A total of 47 factors were identified in six groups as relevant for adherence: (i) sociodemographic (n = 7), (ii) physical activity (n = 2), (iii) dietary (n = 8), (iv) behavioral (n = 4), (v) pharmacological (n = 3), and (vi) multi-intervention (n = 23). In addition, a map of adherence factors was created. The main findings are that with respect to demographic factors, the development of personalized intervention strategies based on the characteristics of specific populations is encouraged. Moreover, self-monitoring has been shown to be effective in behavioral, dietary, and multi-interventions, while technology has shown potential in dietary, behavioral, and multi-interventions. In addition, multi-interventions are adherence-promoting strategies, although more evidence is required on adherence to pharmacological interventions. Overall, the factor map can be controlled and modified by researchers and practitioners to improve adherence to weight loss interventions.

Uncertainty quantification in multi-class segmentation: Comparison between Bayesian and non-Bayesian approaches in a clinical perspective.

BACKGROUND: Automatic segmentation techniques based on Convolutional Neural Networks (CNNs) are widely adopted to automatically identify any structure of interest from a medical image, as they are not time consuming and not subject to high intra- and inter-operator variability. However, the adoption of these approaches in clinical practice is slowed down by some factors, such as the difficulty in providing an accurate quantification of their uncertainty. PURPOSE: This work aims to evaluate the uncertainty quantification provided by two Bayesian and two non-Bayesian approaches for a multi-class segmentation problem, and to compare the risk propensity among these approaches, considering CT images of patients affected by renal cancer (RC). METHODS: Four uncertainty quantification approaches were implemented in this work, based on a benchmark CNN currently employed in medical image segmentation: two Bayesian CNNs with different regularizations (Dropout and DropConnect), named BDR and BDC, an ensemble method (Ens) and a test-time augmentation (TTA) method. They were compared in terms of segmentation accuracy, using the Dice score, uncertainty quantification, using the ratio of correct-certain pixels (RCC) and incorrect-uncertain pixels (RIU), and with respect to inter-observer variability in manual segmentation. They were trained with the Kidney and Kidney Tumor Segmentation Challenge launched in 2021 (Kits21), for which multi-class segmentations of kidney, RC, and cyst on 300 CT volumes are available. Moreover, they were tested considering this and other two public renal CT datasets. RESULTS: Accuracy results achieved large differences across the structures of interest for all approaches, with an average Dice score of 0.92, 0.58, and 0.21 for kidney, tumor, and cyst, respectively. In terms of uncertainties, TTA provided the highest uncertainty, followed by Ens and BDC, whereas BDR provided the lowest, and minimized the number of incorrect certain pixels worse than the other approaches. Again, large differences were seen across the three structures in terms of RCC and RIU. These metrics were associated with different risk propensity, as BDR was the most risk-taking approach, able to provide higher accuracy in its prediction, but failing to assign uncertainty on incorrect segmentation in every case. The other three approaches were more conservative, providing large uncertainty regions, with the drawback of giving alert also on correct areas. Finally, the analysis of the inter-observer segmentation variability showed a significant variation among the four approaches on the external dataset, with BDR reporting the lowest agreement (Dice = 0.82), and TTA obtaining the highest score (Dice = 0.94). CONCLUSIONS: Our outcomes highlight the importance of quantifying the segmentation uncertainty and that decision-makers can choose the approach most in line with the risk propensity degree required by the application and their policy.