BackgroundPatients often express strong preferences for the forms of treatment available for their disease. Incorporating these preferences into the process of treatment decision-making might improve patients' adherence to treatment, contributing to better outcomes. We describe the methodology used in a study aiming to assess treatment outcomes when patients' preferences for treatment are closely matched to recommended treatments.MethodParticipants included patients with moderate and severe psoriasis attending outpatient dermatology clinics at the University Medical Centre Mannheim, University of Heidelberg, Germany. A self-administered online survey used conjoint analysis to measure participants' preferences for psoriasis treatment options at the initial study visit. Physicians' treatment recommendations were abstracted from each participant's medical records. The Preference Matching Index (PMI), a measure of concordance between the participant's preferences for treatment and the physician's recommended treatment, was determined for each participant at t1 (initial study visit). A clinical outcome measure, the Psoriasis Area and Severity Index, and two participant-derived outcomes assessing treatment satisfaction and health related quality of life were employed at t1, t2 (twelve weeks post-t1) and t3 (twelve weeks post-t2). Change in outcomes was assessed using repeated measures analysis of variance. The association between participants' PMI scores at t1 and outcomes at t2 and t3 was evaluated using multivariate regressions analysis.DiscussionWe describe methods for capturing concordance between patients' treatment preferences and recommended treatment and for assessing its association with specific treatment outcomes. The methods are intended to promote the incorporation of patients' preferences in treatment decision-making, enhance treatment satisfaction, and improve treatment effectiveness through greater adherence.

ABSTRACT. Objective. During recent years, coincident with the recommendation to position infants supine, the incidence of posterior deformational plagiocephaly has increased dramatically. The purpose of our study was to determine whether early signs of cranial flattening could be detected in healthy neonates and to document incidence and potential risk factors.Design. A cross-sectional study was performed in healthy newborns. Physical findings, anthropometric cranial measurements, and data on pregnancy and birth were recorded.Results. The incidence of localized cranial flattening in singletons was 13%; other anomalous head shapes were found in 11% of single-born neonates. In twins, localized flat areas were much more frequent with an incidence of 56%. The following risk factors for cranial deformation were identified: assisted vaginal delivery, prolonged labor, unusual birth position, primiparity, and male gender. ABBREVIATIONS. SIDS, sudden infant death syndrome; TCD, transcranial difference. Conclusion. We propose that localized lateral or occipital cranial flattening at birth is a precursor to

The majority of Merkel cell carcinomas (MCCs) are associated with the recently identified Merkel cell polyomavirus (MCV). However, as it is still unclear to which extent the presence of MCV impacts tumor characteristics or clinical outcome, we correlated the MCV status of tumor lesions obtained from 174 MCC patients including 38 MCC patients from Australia and 138 MCC patients from Germany with clinical characteristics, histomorphology, immunohistochemistry, and course of the disease. MCV DNA was present in 86% of MCCs and, in contrast to previous reports, no significant difference in MCV prevalence was present between Australian and German MCC cases. When patients were stratified according to their MCV status, only tumor localization (P=0.001), gender (P=0.024), and co-morbidity, i.e., frequency of patients with previous skin tumors (P=0.024), were significantly different factors. In contrast, year of birth and diagnosis, age at diagnosis, or histological type and features representing the oncogenic phenotype such as mitotic rate or expression of p16, p53, RB1, and Ki67 were not significantly different between MCV-positive and MCV-negative MCCs. MCV status also did not influence recurrence-free, overall, and MCC-specific survival significantly. In summary, although MCV-positive and MCV-negative MCCs may have different etiologies, these tumors have comparable clinical behaviors and prognosis.

Background: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification. Methods: Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300