Cerebral palsy is a common neurodevelopmental condition encountered by pediatricians. The condition may present itself in many different clinical spectra. The etiological and risk factors are many and an awareness of the interplay of multiple factors in the causation of CP is crucial. In many cases, the cause of Cerebral palsy may not be apparent. Cerebral palsy is invariably associated with many deficits such as mental retardation, speech and language and oromotor problems. A thorough neurodevelopmental assessment of the child with Cerebral Palsy should include evaluation of associated deficits so that a comprehensive early intervention program an be planned and executed.

Research in the field of neurosciences and genetics has given us great insight into the understanding of learning and behavior and changes in the brain in response to experience. It is seen that brain is dynamically changing throughout life and is capable of learning at any time. Critical periods of neuroplasticity for various streams of development are also better understood. Technological advances in non invasive imaging techniques and advances in molecular genetics have helped us understand the basis of many developmental disorders which may help in planning effective intervention strategies.

Evidence before this study: Acute appendicitis is the most common general surgical emergency in children. Its diagnosis remains challenging and children presenting with acute right iliac fossa (RIF) pain may be admitted for clinical observation or undergo normal appendicectomy (removal of a histologically normal appendix). A search for external validation studies of risk prediction models for acute appendicitis in children was performed on MEDLINE and Web of Science on 12 January 2017 using the search terms ["appendicitis" OR "appendectomy" OR "appendicectomy"] AND ["score" OR "model" OR "nomogram" OR "scoring"]. Studies validating prediction models aimed at differentiating acute appendicitis from all other causes of RIF pain were included. No date restrictions were applied. Validation studies were most commonly performed for the Alvarado, Appendicitis Inflammatory Response Score (AIRS), and Paediatric Appendicitis Score (PAS) models. Most validation studies were based on retrospective, single centre, or small cohorts, and findings regarding model performance were inconsistent. There was no high quality evidence to guide selection of the optimum model and threshold cutoff for identification of low-risk children in the UK and Ireland. Added value of this study: Most children admitted to hospital with RIF pain do not undergo surgery. When children do undergo appendicectomy, removal of a normal appendix (normal appendicectomy) is common, occurring in around 1 in 6 children. The Shera score is able to identify a large low-risk group of children who present with acute RIF pain but do not have acute appendicitis (specificity 44%). This low-risk group has an overall 1 in 30 risk of acute appendicitis and a 1 in 270 risk of perforated appendicitis. The Shera score is unable to achieve a sufficiently high positive predictive value to select a high-risk group who should proceed directly to surgery. Current diagnostic performance of ultrasound is also too poor to select children for surgery. Implications of all the available evidence: Routine pre-operative risk scoring could inform shared decision making by doctors, children, and parents by supporting safe selection of lowrisk patients for ambulatory management, reducing unnecessary admissions and normal appendicectomy. Hospitals should ensure seven-day-a-week availability of ultrasound for medium and high-risk patients. Ultrasound should be performed by operators trained to assess for acute appendicitis in children. For children in whom diagnostic uncertainty remains following ultrasound, magnetic resonance imaging (MRI) or low-dose computed tomography (CT) are second-line investigations.

To support the global restart of elective surgery, data from an international prospective cohort study of 8492 patients (69 countries) was analysed using artificial intelligence (machine learning techniques) to develop a predictive score for mortality in surgical patients with SARS-CoV-2. We found that patient rather than operation factors were the best predictors and used these to create the COVIDsurg Mortality Score (https://covidsurgrisk.app). Our data demonstrates that it is safe to restart a wide range of surgical services for selected patients.

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