Using patient-generated health data more efficient and effectively to facilitate the implementation of value-based healthcare in the EU - Innovation report.

Healthcare services and products are rapidly changing due to the development of new technologies, offering relevant solutions to improve patient outcomes. Patient-Generated Health Data and knowledge-sharing across the European Union (EU) has a great potential of making healthcare provision more effective and efficient by putting the patient at the centre of the healthcare process. While such initiatives have been taken before, a uniting and overarching approach is still missing.

The prevalence of posttraumatic stress disorder symptomatology and diagnosis in burn survivors: a systematic review and meta-analysis.

Great disparity is observed among studies investigating the prevalence of PTSD after burns. This systematic review and meta-analysis aimed to explore the pooled prevalence of PTSD in adult burn survivors over the first two years post-burn. Five electronic databases were searched for observational studies assessing the prevalence of PTSD symptoms after burns.

Towards accurate screening and prevention for PTSD (2-ASAP): protocol of a longitudinal prospective cohort study.

Background: Effective preventive interventions for PTSD rely on early identification of individuals at risk for developing PTSD. To establish early post-trauma who are at risk, there is a need for accurate prognostic risk screening instruments for PTSD that can be widely implemented in recently trauma-exposed adults. Achieving such accuracy and generalizability requires external validation of machine learning classification models.

Long-term PTSD prevalence and associated adverse psychological, functional, and economic outcomes: a 12-15 year follow-up of adults with suspected serious injury.

An increasing number of longitudinal studies investigates long-term PTSD, related outcomes and potential gender differences herein. However, a knowledge gap exists when it comes to studies following individual civilian trauma beyond a decade post-trauma. To investigate the long-term PTSD prevalence, associated adverse psychological, functional and economic outcomes related to (suspected) serious injury of 12-15 years ago in Dutch adults, as well as potential gender differences herein.

Machine learning to optimize literature screening in medical guideline development.

Objectives: In a time of exponential growth of new evidence supporting clinical decision-making, combined with a labor-intensive process of selecting this evidence, methods are needed to speed up current processes to keep medical guidelines up-to-date. This study evaluated the performance and feasibility of active learning to support the selection of relevant publications within medical guideline development and to study the role of noisy labels.

Design: We used a mixed-methods design.

Impact of Active learning model and prior knowledge on discovery time of elusive relevant papers: a simulation study.

Software that employs screening prioritization through active learning (AL) has accelerated the screening process significantly by ranking an unordered set of records by their predicted relevance. However, failing to find a relevant paper might alter the findings of a systematic review, highlighting the importance of identifying elusive papers. The time to discovery (TD) measures how many records are needed to be screened to find a relevant paper, making it a helpful tool for detecting such papers.

The SAFE procedure: a practical stopping heuristic for active learning-based screening in systematic reviews and meta-analyses.

Active learning has become an increasingly popular method for screening large amounts of data in systematic reviews and meta-analyses. The active learning process continually improves its predictions on the remaining unlabeled records, with the goal of identifying all relevant records as early as possible. However, determining the optimal point at which to stop the active learning process is a challenge.

Addressing the challenges of reconstructing systematic reviews datasets: a case study and a noisy label filter procedure.

Systematic reviews and meta-analyses typically require significant time and effort. Machine learning models have the potential to enhance screening efficiency in these processes. To effectively evaluate such models, fully labeled datasets-detailing all records screened by humans and their labeling decisions-are imperative.

Which individual, social, and urban factors in early childhood predict psychopathology in later childhood, adolescence and young adulthood? A systematic review.

Background: A comprehensive picture is lacking of the impact of early childhood (age 0-5) risk factors on the subsequent development of mental health symptoms.

Objective: In this systematic review, we investigated which individual, social and urban factors, experienced in early childhood, contribute to the development of later anxiety and depression, behavioural problems, and internalising and externalising symptoms in youth.

Methods: Embase, MEDLINE, Scopus, and PsycInfo were searched on the 5 of January 2022.

Performance of active learning models for screening prioritization in systematic reviews: a simulation study into the Average Time to Discover relevant records.

Background: Conducting a systematic review demands a significant amount of effort in screening titles and abstracts. To accelerate this process, various tools that utilize active learning have been proposed. These tools allow the reviewer to interact with machine learning software to identify relevant publications as early as possible.

Active learning-based systematic reviewing using switching classification models: the case of the onset, maintenance, and relapse of depressive disorders.

Introduction: This study examines the performance of active learning-aided systematic reviews using a deep learning-based model compared to traditional machine learning approaches, and explores the potential benefits of model-switching strategies.

Methods: Comprising four parts, the study: 1) analyzes the performance and stability of active learning-aided systematic review; 2) implements a convolutional neural network classifier; 3) compares classifier and feature extractor performance; and 4) investigates the impact of model-switching strategies on review performance.

Results: Lighter models perform well in early simulation stages, while other models show increased performance in later stages.

Measurement invariance in the social sciences: Historical development, methodological challenges, state of the art, and future perspectives.

This review summarizes the current state of the art of statistical and (survey) methodological research on measurement (non)invariance, which is considered a core challenge for the comparative social sciences. After outlining the historical roots, conceptual details, and standard procedures for measurement invariance testing, the paper focuses in particular on the statistical developments that have been achieved in the last 10 years. These include Bayesian approximate measurement invariance, the alignment method, measurement invariance testing within the multilevel modeling framework, mixture multigroup factor analysis, the measurement invariance explorer, and the response shift-true change decomposition approach.

Sex-differential PTSD symptom trajectories across one year following suspected serious injury.

Background: Recent years have shown an increased application of prospective trajectory-oriented approaches to posttraumatic stress disorder (PTSD). Although women are generally considered at increased PTSD risk, sex and gender differences in PTSD symptom trajectories have not yet been extensively studied.

Objective: To perform an in-depth investigation of differences in PTSD symptom trajectories across one-year post-trauma between men and women, by interpreting the general trends of trajectories observed in sex-disaggregated samples, and comparing within-trajectory symptom course and prevalence rates.

Bio-Psychological Predictors of Acute and Protracted Fatigue After Burns: A Longitudinal Study.

Objective: Fatigue after burns is often attributed to the hyperinflammatory and hypermetabolic response, while it may be best understood from a bio-psychological perspective, also involving the neuro-endocrine system. This longitudinal multi-center study examined the course of fatigue up to 18 months postburn. The contribution of bio-psychological factors, including burn severity, pain, and acute PTSD symptoms, to the course and persistence of fatigue was studied in a multifactorial model.

The Use of Questionable Research Practices to Survive in Academia Examined With Expert Elicitation, Prior-Data Conflicts, Bayes Factors for Replication Effects, and the Bayes Truth Serum.

The popularity and use of Bayesian methods have increased across many research domains. The current article demonstrates how some less familiar Bayesian methods can be used. Specifically, we applied expert elicitation, testing for prior-data conflicts, the Bayesian Truth Serum, and testing for replication effects via Bayes Factors in a series of four studies investigating the use of questionable research practices (QRPs).

Approximate Measurement Invariance of Willingness to Sacrifice for the Environment Across 30 Countries: The Importance of Prior Distributions and Their Visualization.

Nationwide opinions and international attitudes toward climate and environmental change are receiving increasing attention in both scientific and political communities. An often used way to measure these attitudes is by large-scale social surveys. However, the assumption for a valid country comparison, measurement invariance, is often not met, especially when a large number of countries are being compared.

Prevalence and course of posttraumatic stress disorder symptoms in partners of burn survivors.

: Partners of burn survivors may develop posttraumatic stress disorder (PTSD) symptoms in response to the potential life threatening nature of the burn event and the burn survivor's medical treatment. : This longitudinal study examined the prevalence, course and potential predictors of partners' PTSD symptoms up to 18 months post-burn. : Participants were 111 partners of adult burn survivors.

On Imbalance of Impulse Control and Sensation Seeking and Adolescent Risk: An Intra-individual Developmental Test of the Dual Systems and Maturational Imbalance Models.

Heterogeneity in development of imbalance between impulse control and sensation seeking has not been studied until now. The present study scrutinized this heterogeneity and the link between imbalance and adolescent risk. Seven-wave data of 7,558 youth (50.

Individual recovery of health-related quality of life during 18 months post-burn using a retrospective pre-burn measurement: an exploratory study.

Purpose: This study explored the individual trajectories of health-related quality of life (HRQL) compared to recalled pre-burn level of HRQL and investigated whether burn severity and post-traumatic stress disorder (PTSD) symptoms increase the risk of not returning to pre-burn level of HRQL.

Methods: Data were obtained from 309 adult patients with burns in a multicenter study. Patients completed the EQ-5D-3L questionnaire with a Cognition bolt-on shortly after hospital admission, which included a recalled pre-injury measure, and, again, at 3, 6, 12 and 18 months post-burn.

The data representativeness criterion: Predicting the performance of supervised classification based on data set similarity.

In a broad range of fields it may be desirable to reuse a supervised classification algorithm and apply it to a new data set. However, generalization of such an algorithm and thus achieving a similar classification performance is only possible when the training data used to build the algorithm is similar to new unseen data one wishes to apply it to. It is often unknown in advance how an algorithm will perform on new unseen data, being a crucial reason for not deploying an algorithm at all.

Expert Elicitation for Latent Growth Curve Models: The Case of Posttraumatic Stress Symptoms Development in Children With Burn Injuries.

Experts provide an alternative source of information to classical data collection methods such as surveys. They can provide additional insight into problems, supplement existing data, or provide insights when classical data collection is troublesome. In this paper, we explore the (dis)similarities between expert judgments and data collected by traditional data collection methods regarding the development of posttraumatic stress symptoms (PTSSs) in children with burn injuries.

Classification of comorbidity in obsessive-compulsive disorder: A latent class analysis.

Objective: Patients with OCD differ markedly from one another in both number and kind of comorbid disorders. In this study, we set out to identify and characterize homogeneous subgroups of OCD patients based on their comorbidity profile.

Methods: In a cohort of 419 adult subjects with OCD, the lifetime presence of fifteen comorbid disorders was assessed.