In 2019, the results of the first James Lind Alliance (JLA) research priorities setting partnership (PSP) in paediatric orthopaedics were published in the form of 10 top questions. One of which was, "What is the clinical and cost effectiveness of pre-operative rehabilitation in children presenting with lower limb orthopaedic conditions?" The purpose of this study was to assess the clinical and cost effectiveness of simple pre-operative rehabilitation (prehabilitation) strategies, as measured by hospital Length of Stay (LoS). Patients and Methods: Clinical records were reviewed retrospectively to determine the average LoS for patients who underwent lower limb paediatric orthopaedic surgery before any prehabilitation intervention. Prehabilitation intervention strategies were introduced, including patient education, provision of crutches and goal setting and subsequent data was collected. LoS before and after intervention were compared. In addition, extra bed days, defined as the difference between actual and expected LoS (determined by the treating clinician with reference to national data), were compared. Results: Before intervention, the average LoS after paediatric orthopaedic surgery in our hospital was 2.95 days (range 12 days; standard deviation (SD) 2.20 days) and after intervention, the average LoS was reduced to 2.70 days (range 7 days; SD 1.84 days). These data showed an 8% reduction in hospital LoS after the introduction of prehabilitation. Further, there was a significant (p=0.024) reduction in extra bed days (per patient) from 1.12 days (range 14 days; SD 1.61 days) before intervention to 0.72 days (range 8 days; SD 1.45 days) after intervention, equating to an estimated saving of £46,500 in hospital bed costs only. Conclusion:This data indicates that simple prehabilitation strategies can reduce hospital LoS by 8% indicating improved clinical outcomes. Further, extra bed days may be reduced by 36% with potentially important cost savings.

Magnetic resonance imaging (MRI)-based brain segmentation has recently been revolutionized by deep learning methods. These methods use large numbers of annotated segmentations to train algorithms that have the potential to perform brain segmentations reliably and quickly. However, training data for these algorithms are frequently obtained from automated brain segmentation systems, which may contain inaccurate neuroanatomy. Thus, the neuroimaging community would benefit from an open source database of high quality, neuroanatomically curated and manually edited MRI brain images, as well as the publicly available tools and detailed procedures for generating these curated data. Manual segmentation approaches are regarded as the gold standard for brain segmentation and parcellation. These approaches underpin the construction of neuroanatomically accurate human brain atlases. In addition, neuroanatomically precise definitions of MRI-based regions of interest (ROIs) derived from manual brain segmentation are essential for accuracy in structural connectivity studies and in surgical planning for procedures such as deep brain stimulation. However, manual segmentation procedures are time and labor intensive, and not practical in studies utilizing very large datasets, large cohorts, or multimodal imaging. Automated segmentation methods were developed to overcome these issues, and provide high data throughput, increased reliability, and multimodal imaging capability. These methods utilize manually labeled brain atlases to automatically parcellate the brain into different ROIs, but do not have the anatomical accuracy of skilled manual segmentation approaches. In the present study, we developed a custom software module for manual editing of brain structures in the freely available 3D Slicer software platform that employs principles and tools based on pioneering work from the Center for Morphometric Analysis (CMA) at Massachusetts General Hospital. We used these novel 3D Slicer segmentation tools and techniques in conjunction with well-established neuroanatomical definitions of subcortical brain structures to manually segment 50 high resolution T1w MRI brains from the Human Connectome Project (HCP) Young Adult database. The structural definitions used herein are associated with specific neuroanatomical ontologies to systematically interrelate histological and MRI-based morphometric definitions. The resulting brain datasets are publicly available and will provide the basis for a larger database of anatomically curated brains as an open science resource.

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A. Rose, in my judgment, raises a tempest in a teapot about nothing. In a very learned analysis he condemns the use of the word appendicitis and prefers perityphlitis instead. I think the learned gentleman's labors waste themselves on mere logomachy. In my opinion the term "appendicitis" describes the condition sufficiently well for all practical purposes. He prefers perityphlitis, which means inflammation around, peri ([unk]) the cecum, tuphlon ([unk]) blind. Now, the appendix is not about the cecum but directly attached to it, and there is no doubt but that the term appendicitis describes the condition accurately: If Dr. Rose is not satisfied etymologically why not call it epityphlitis, epi ([unk]), on or attached to the cecum, tuphlon ([unk]). Or perhaps he objects to the term appendicitis as being a hybrid word, half Latin, half Greek, which nauseates his exquisitely literary stomach.

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