This naturalistic study assesses the effectiveness of inpatient multidisciplinary treatment of children and adolescents with somatic symptom disorders (SSD) and investigates the role of pain coping strategies and psychiatric comorbidity (anxiety, depression). Sixty children and adolescents (mean age 14.4 years) with SSD who underwent inpatient multidisciplinary treatment were assessed regarding their school attendance, levels of discomfort, coping strategies and psychiatric comorbidity (depression, anxiety) at pretreatment, discharge and 6 months following treatment. At discharge, the children and adolescents reported improvements in their level of discomfort, psychiatric comorbidities (anxiety, depression) and pain coping strategies, with medium to large effect sizes. Six months following treatment, the improvements remained stable, including significantly higher school attendance rates (d = 1.6; p < 0.01). Improvement in pain coping was associated with increased school attendance. Inpatient multidisciplinary treatment is effective in reducing levels of discomfort, psychiatric comorbidity (anxiety, depression), and school absence and in improving coping strategies.

Background. A growing body of evidence suggests an association between altered hypothalamic-pituitary-adrenal axis reactivity and the development of persistent antisocial behavior in children. However the effects of altered cortisol levels remain poorly understood in the complex context of conduct disorder, callous-unemotional (CU) personality traits, and frequent comorbidities, such as attention deficit hyperactivity disorder (ADHD). The aim of the current study was to investigate associations among CU traits, antisocial behavior, and comorbid ADHD symptomatology with cortisol levels in male children and adolescents.Methods. The study included 37 boys with early-onset conduct disorder (EO-CD, mean age 11.9 years) and 38 healthy boys (mean age 12.5 years). Participants were subjected to multiple daytime salivary cortisol measurements and a psychometric characterization.Results. Subjects in the EO-CD group with elevated CU traits showed a diminished cortisol awakening response compared to healthy participants. In the EO-CD group, high CU traits and impulsivity were associated with decreased diurnal cortisol levels, while associations with antisocial behavior were not detected. The cortisol awakening response was significantly inversely associated with hyperactivity (P=0.02) and marginally significant with CU traits (P=0.07).Conclusions. These results indicate a specific association between CU traits and a diminished stress response, which is not explained by antisocial behavior in general.

Background: Machine learning (ML) approaches are a crucial component of modern data analysis in many fields, including epidemiology and medicine. Nonlinear ML methods often achieve accurate predictions, for instance, in personalized medicine, as they are capable of modeling complex relationships between features and the target. Problematically, ML models and their predictions can be biased by confounding information present in the features. To remove this spurious signal, researchers often employ featurewise linear confound regression (CR). While this is considered a standard approach for dealing with confounding, possible pitfalls of using CR in ML pipelines are not fully understood. Results: We provide new evidence that, contrary to general expectations, linear confound regression can increase the risk of confounding when combined with nonlinear ML approaches. Using a simple framework that uses the target as a confound, we show that information leaked via CR can increase null or moderate effects to near-perfect prediction. By shuffling the features, we provide evidence that this increase is indeed due to confound-leakage and not due to revealing of information. We then demonstrate the danger of confound-leakage in a real-world clinical application where the accuracy of predicting attention-deficit/hyperactivity disorder is overestimated using speech-derived features when using depression as a confound. Conclusions: Mishandling or even amplifying confounding effects when building ML models due to confound-leakage, as shown, can lead to untrustworthy, biased, and unfair predictions. Our expose of the confound-leakage pitfall and provided guidelines for dealing with it can help create more robust and trustworthy ML models.