Michael Barrett

Last week’s Danish general election left the identity of the new government dependent on negotiations between the elected parties, leaving a possible opening for the two representatives from Greenland.

MitID, the digital ID system used to access public service platforms and make online payments in Denmark, has been inaccessible for much of Tuesday.

While European countries implement political measures to counter high fuel prices caused by the war in the Middle East, are Denmark’s options limited by the fact it currently has an interim government?

Copenhagen Airport has now fully implemented the EES border check system after a partial roll-out in October last year, meaning that non-EU travellers arriving in the country from outside the Schengen Area will be fingerprinted at border control.

Background The original research priorities for paediatric emergency medicine (PEM) in the UK and Ireland were published in 2015. This list, generated through a modified Delphi process involving healthcare professionals only, has driven the research agenda for over ten years. With many now successfully addressed and a significantly altered healthcare landscape, there was a pressing need to refresh PEM research priorities with the input of patients and carers. Methods James Lind Alliance (JLA) methodology was employed. The scope included unscheduled emergency care provided to children and young people irrespective of setting. An independent JLA facilitator chaired monthly steering group meetings with equal input from parents and healthcare professionals. Two online surveys were employed to generate a list of evidence uncertainties and then to prioritise research questions from key stakeholder groups (patients, carers and healthcare professionals). An online workshop subsequently used an adapted nominal group technique to reach a consensus on the top list of research priorities. Results 655 questions were submitted in Survey 1 by 338 respondents (35% patients and carers and 65% healthcare professionals). After merging questions by topic and removing out-of-scope questions, 70 summary questions proceeded to evidence reviews; three were found to have been sufficiently answered. Further merging of summary questions resulted in 46 indicative research questions for Survey 2, which received 542 complete responses (26.6% patients and carers, 73.4% healthcare professionals). The 18 highest-ranking questions were brought to the consensus workshop, in which 12 patients/carers and 15 healthcare professionals reached consensus on the 10 highest priority research questions. Conclusion This refreshed PEM prioritisation study has identified the top 10 research priorities reflecting the views of patients, carers and a range of health professionals across the UK and Ireland. These priorities will be used to drive the PEM research agenda for the next decade. Data are available upon reasonable request. Supplementary data are available upon request.

Objectives To describe the incidence of new onset paediatric diabetes mellitus, clinical characteristics and patterns of presentation to emergency departments (ED) during the COVID-19 pandemic, and to assess whether this increase was associated with SARS-CoV-2 infection. Design Retrospective medical record review. Setting Forty nine paediatric EDs across the UK and Ireland. Patients All children aged 6 months to 16 years presenting to EDs with (1) new onset diabetes or (2) pre-existing diabetes with diabetic ketoacidosis (DKA), during the COVID-19 pandemic (1 March 2020–28 February 2021) and the preceding year (1 March 2019–28 February 2020). Results There were increases in new onset diabetes (1015 to 1183, 17%), compared with background incidence of 3%–5% in the UK over the past 5 years. There were increases in children presenting with new onset diabetes in DKA (395 to 566, 43%), severe DKA (141 to 252, 79%) and admissions to intensive care (38 to 72, 89%). Increased severity was reflected in biochemical and physiological parameters and administration of fluid boluses. Time to presentation from symptom onset for children presenting with new onset diabetes and DKA were similar across both years; healthcare seeking delay did not appear to be the sole contributing factor to DKA during the pandemic. Patterns of presentation changed in the pandemic year and seasonal variation was lost. Children with pre-existing diabetes presented with fewer episodes of decompensation. Conclusions There were increases in new onset diabetes in children and a higher risk of DKA in the first COVID pandemic year. Data are available upon reasonable request. Not applicable.

Students and staff enjoyed the Red Lake Secondary Complex PowWow! Miigwech to everyone who came - including drum and students from RLEC and the charter school!

IF you love both quaint English pubs and period films, then a stay at the Pilsley Inn is a must. Here is everything you need to know about a stay at the pub in Derbyshire. Where is the Pilsley Inn?…

This review synthesises the range of regulatory, technical and ethical sources that govern artificial intelligence (AI) use in paediatrics, given the current absence of one unified paediatric-specific framework (table 1). AI is increasingly present in paediatrics, particularly in imaging, early warning systems and clinical decision support. However, most AI systems are developed and validated in adults, raising concerns about performance, fairness and safety when applied to children. Paediatricians therefore require an understanding of regulatory, methodological and ethical frameworks that shape AI use in clinical practice. View this table: Table 1 Frameworks at a glance The National Institute for Health and Care Excellence (NICE) Evidence Standards Framework (ESF) sets out national expectations for digital health technologies.1 The 2022 update incorporated requirements for AI and adaptive algorithms, with expectations for clear documentation of datasets, representativeness, bias and performance monitoring.1 AI systems used for diagnosis, prediction or triage usually fall under Tier C, which sets the highest evidence expectations, including evidence of clinical effectiveness and real-world monitoring.1 ESF also highlights the need for safeguarding of vulnerable groups including children. Regulatory oversight is provided by the Medicines and Healthcare products Regulatory Agency (MHRA software) and AI as a Medical Device (SaMD/AiMD) programme, which governs AI systems that meet the definition of a medical device.2 SaMD/AiMD Change Programme outlines how the UK will regulate adaptive algorithms, transparency and post-market surveillance.2 For clinicians, this includes an expectation to report unexpected outputs or harm through the Yellow Card Scheme, particularly relevant to paediatrics where developmental variation may alter performance. Complementing these regulatory approaches, the British Standards Institute’s ‘BS 30440:2023 Validation framework for use of AI within healthcare’ provides an auditable lifecycle standard for healthcare AI.3 It sets out expectations from problem definition through development, validation, deployment and monitoring. The standard expects consideration of subgroup performance, bias …

All Red Lake District #38 Schools held Open House at all locations on Wednesday, September 2, 2026 from 1 PM to 6:30 PM.