The Capital Region of Denmark (Danish: Region Hovedstaden, pronounced [ʁekiˈoˀn ˈhoːð̩ˌstæðˀn̩]) is the easternmost administrative region of Denmark. The Capital Region has 29 municipalities. The regional council consists of 41 elected politicians. The chairperson as of 1 August 2021 is Lars Gaardhøj. He is a member of Social Democrats.The Capital Region was established on 1 January 2007 as part of the 2007 Danish Municipal Reform, which abolished the traditional counties (Danish plural: amter, singular: amt) and set up five regions. At the same time, smaller municipalities were merged into larger units, cutting the number of municipalities from 271 before 1 January 2006, when Ærø Municipality was created, to 98. The reform diminished the power of the regional level dramatically in favor of the local level and the central government in Copenhagen. The reform was implemented on 1 January 2007. The main task for the Danish regions are hospitals and healthcare. It is not to be confused with the Copenhagen Metropolitan Area nor with the Øresund Region. Unlike the counties (1970-2006) (Danish Amtskommune [da] literally county municipality) the regions are not municipalities and are thus not allowed to have coat of arms, but only logotypes, and cannot "shuffle money around" from one area of expenditure to another area of expenditure, that is, use money for any other purpose than has been stated specifically, but must pay money not used back rather like departments or agencies of the central government. The regions do not levy any taxes but are financed only through block grants.
Abstract Objective Acute heart failure (AHF) is a common but underrecognized cause of dyspnea. Chest computed tomography (CT) can accurately assess pulmonary congestion, but radiologist reporting capacity may limit clinical utility. We hypothesized that an artificial intelligence (AI) model could automatically detect imaging signs of AHF and aimed to prospectively validate an AI model in an independent emergency department cohort, benchmarking its performance against radiologists and cardiologists. Materials and methods We prospectively validated a supervised machine-learning model in a single-center study of dyspneic patients undergoing low-dose, non-contrast chest CT and echocardiography. The primary analysis assessed diagnostic performance for CT-detected pulmonary congestion compatible with AHF, using radiologist-reported AHF as the reference and the area under the curve at receiver operating characteristic analysis (AUROC). Secondary analyses compared the AI model with blinded research radiologists and expert cardiologists. Results Of 234 patients (56% males), aged 74 ± 10 years (mean ± standard deviation), 61 (26%) had radiologist-reported AHF. The AI model achieved high diagnostic performance (AUROC 0.95 [95% confidence interval 0.93–0.98]), with 89% sensitivity [78–95] and 89% specificity [83–93]. At prespecified thresholds, rule-out maximized sensitivity (97% [89–100]) at the expense of specificity (74% [67–81]), whereas rule-in yielded high specificity (96% [92–98]) but lower sensitivity (66% [52–77]). In secondary analyses, the AI model achieved a median AUROC of 0.94 (range 0.91–0.96). Conclusion The AI model demonstrated high diagnostic performance for detecting AHF on chest CT in dyspneic patients. Integration into emergency workflows may support more consistent diagnosis, independent of clinician experience or time constraints. Relevance statement AI-based analysis of chest CT may enable earlier and more consistent detection of AHF, supporting timely triage and management, especially when specialist radiological expertise is limited or delayed. Key Points An AI model prospectively detected AHF on chest CT in dyspneic emergency department patients. In a prospective single-center cohort, AI achieved high diagnostic performance (AUROC 0.91–0.96), comparable to that of radiologists and cardiologists. AI-based chest CT interpretation may improve diagnostic consistency in the absence of standardized CT criteria for AHF. Graphical Abstract
The European Society for Child and Adolescent Psychiatry (ESCAP) clinical guidance on transition supports care across the child and adolescent mental health service (CAMHS) and adult mental health service (AMHS) boundary throughout Europe. It outlines practices, procedures, and service environments to promote appropriate, safe and timely transition of young people from child and adolescent to adult mental health services or alternative care settings. The guidance addresses planning, decision-making and discharge management at CAMHS and, where needed, continuity in AMHS. The development of the guidance followed established methodological standards for clinical guidance production, combining evidence review, patient and public involvement (PPI), and expert consensus through a structured four-stage process. The guidance is presented in two parts: Part 1 covers six key domains of transition practice, while Part 2 targets service improvement. Intended primarily for clinicians and service managers and providers, the guidance also offers useful information for young people and their families. To enhance local relevance, countries should adapt recommendations to national service and policy contexts.
Colorectal cancer (CRC) is the third most diagnosed malignancy and the second leading cause of cancer-related death worldwide. As global screening programs expand, there is an increasing need for validated tools that ensure colonoscopy competence before trainees perform procedures on patients. This study examined validity evidence for a simulation-based colonoscopy test using a novel colon phantom with automated performance metrics. In this prospective observational study, 42 participants from Denmark, Norway, and Sweden were categorized as novice (0 procedures), intermediate (1–999), or experienced (>1000). Each completed three colonoscopy cases (EASY, STANDARD, ADVANCED 1) using the Mikoto simulator, which combines realistic haptics with automatic recording of key metrics: time to reach the caecum and the Sigmoid-Colon Elongation Index, a refined measure of unnecessary force and excessive stretching during scope advancement—reflecting the gentleness and finesse of scope manipulation. After successful intubation, participants performed a mucosal inspection scored by the PolypTumor Detection Score. Validity evidence was evaluated following Messick’s framework, and pass/fail standards were set using the contrasting groups’ method. Inter-case reliability was excellent (Cronbach’s α = 0.93 for time; 0.83 for elongation index). Simulator metrics clearly discriminated between experience levels (p < 0.001), and strong inverse correlations were observed between time and elongation index (r = − 0.78 to − 0.89), indicating that the fastest participants were also the gentlest. None of the novices passed, while 36
AIM:This study examined theory-driven predictors and moderators of treatment outcomes in group-based cognitive behavioral therapy with exposure and response prevention (CBT/ERP) and acceptance and commitment therapy (ACT) for obsessive-compulsive disorder (OCD). METHOD:A total of 176 adults diagnosed with OCD according to DSM-5 criteria were randomized to either group-based CBT/ERP or ACT. Participants received 14 weekly sessions and were assessed using clinical interviews at baseline, post-treatment, and 6- and 12-month follow-ups. RESULTS:Anxiety sensitivity, experiential avoidance, and emotion regulation difficulties significantly moderated treatment response, with individuals high on these dimensions benefiting more from CBT/ERP than ACT. CONCLUSION:These findings were contrary to our hypotheses and underline the importance of investigating the active mechanisms of ACT. Additionally, group therapeutic alliance predicted and moderated treatment outcomes across both interventions. CLINICAL OR METHODOLOGICAL SIGNIFICANCE OF THIS ARTICLE:This study provides preliminary evidence that anxiety sensitivity, experiential avoidance, and emotion regulation difficulties significantly moderated treatment response, with individuals scoring higher on these dimensions deriving greater benefit from CBT/ERP compared with ACT. These findings were contrary to our hypotheses and underscore the need for further investigation into the mechanisms of action underlying ACT. In addition, group therapeutic alliance moderated outcomes across both interventions.
Elevated emotional distress is common in patients with type 1 diabetes mellitus (T1DM), and alleviating psychological problems is important as they may interfere with adherence to T1DM treatment regiments and poor glycaemic regulation. One way to understand and treat emotional distress is to formulate them as linked to self-regulatory strategies and their underlying metacognitive beliefs as proposed by the Self-regulatory Executive Functioning (S-REF) model. We aimed to evaluate the role of dysfunctional metacognitions in diabetes-related emotional distress beyond general emotional distress and to test the statistical fit of a metacognitive model in a sample consisting of 218 adults with T1DM. Dysfunctional metacognitive beliefs were significantly and positively associated with emotional distress symptoms. Beliefs about the need to control thoughts and cognitive self-consciousness showed a unique association with diabetes-related emotional distress even when controlling for general emotional distress symptoms. A metacognitive model specified by dysfunctional metacognitive beliefs, metacognitive strategies (brooding), and general and diabetes-related emotional distress fitted the data well. The metacognitive model of psychological disorders is relevant to formulate and possibly treat different types of emotional distress symptoms in patients with T1DM with the implication that a feasibility trial of metacognitive therapy is warranted.
