AimsWe conducted a systematic review assessing the reporting quality of studies validating models based on machine learning (ML) for clinical diagnosis, with a specific focus on the reporting of information concerning the participants on which the diagnostic task was evaluated on.MethodMedline Core Clinical Journals were searched for studies published between July 2015 and July 2018. Two reviewers independently screened the retrieved articles, a third reviewer resolved any discrepancies. An extraction list was developed from the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis guideline. Two reviewers independently extracted the data from the eligible articles. Third and fourth reviewers checked, verified the extracted data as well as resolved any discrepancies between the reviewers.ResultsThe search results yielded 161 papers, of which 28 conformed to the eligibility criteria. Detail of data source was reported in 24 of the 28 papers. For all of the papers, the set of patients on which the ML-based diagnostic system was evaluated was partitioned from a larger dataset, and the method for deriving such set was always reported. Information on the diagnostic/non-diagnostic classification was reported well (23/28). The least reported items were the use of reporting guideline (0/28), distribution of disease severity (8/28 patient flow diagram (10/28) and distribution of alternative diagnosis (10/28). A large proportion of studies (23/28) had a delay between the conduct of the reference standard and ML tests, while one study did not and four studies were unclear. For 15 studies, it was unclear whether the evaluation group corresponded to the setting in which the ML test will be applied to.ConclusionAll studies in this review failed to use reporting guidelines, and a large proportion of them lacked adequate detail on participants, making it difficult to replicate, assess and interpret study findings.PROSPERO registration numberCRD42018099167.

The Coronavirus disease 2019 (COVID-19) pandemic is challenging societies, governments and economies at an unprecedented scale. Policy responses to face the epidemic virus had varied greatly among countries, from massive testing, large scale lockdowns to more voluntary approaches for social distancing. The analysis of such policies has been framed mainly with the sole aim of reducing virus transmission (“Flatten the curve” strategies), such as suppression and mitigation approaches. In this article, we argue that the overarching goal of the policy response should not be reducing the viral spread but to ultimately reduce the negative impacts on health and wellbeing of the COVID-19 pandemic. This requires not only interventions to reduce virus transmission, but also policies aimed at increasing the capacity of the health system’s response (“Raising the line” strategies), mitigating the negative consequences of the epidemic and potential adverse effects of interventions to tackle the outbreak (Mitigation strategies) and increasing governmental capacities to respond to the crisis (Strengthening Governance strategies). We propose PoliMap, a comprehensive taxonomy addressing these four policy domains. We present an overview of potential policies in each domain and discuss cross-cutting dimensions of policy responses as the dynamic nature of policymaking, alignment of policy responses at different levels, types of policy instruments, coverage of interventions and gendered-impact of policies. This taxonomy could be used to systematically map, monitor and compare policy responses across countries and over time, in conjunction with morbidity, mortality and demographic data, used to obtain a broad view of the societal effects of COVID-19 pandemic.

BackgroundRed flags are signs and symptoms that are possible indicators of serious spinal pathology. There is limited evidence or guidance on how red flags should be used in practice. Due to the lack of robust evidence for many red flags their use has been questioned. The aim was to conduct a systematic review specifically reporting on studies that evaluated the diagnostic accuracy of red flags for Spinal Infection in patients with low back pain.MethodsSearches were carried out to identify the literature from inception to March 2019. The databases searched were Medline, CINHAL Plus, Web of Science, Embase, Cochrane, Pedro, OpenGrey and Grey Literature Report. Two reviewers screened article texts, one reviewer extracted data and details of each study, a second reviewer independently checked a random sample of the data extracted.ResultsForty papers met the eligibility criteria. A total of 2224 cases of spinal infection were identified, of which 1385 (62%) were men and 773 (38%) were women mean age of 55 (± 8) years. In total there were 46 items, 23 determinants and 23 clinical features. Spinal pain (72%) and fever (55%) were the most common clinical features, Diabetes (18%) and IV drug use (9%) were the most occurring determinants. MRI was the most used radiological test and Staphylococcus aureus (27%), Mycobacterium tuberculosis (12%) were the most common microorganisms detected in cases.ConclusionThe current evidence surrounding red flags for spinal infection remains small, it was not possible to assess the diagnostic accuracy of red flags for spinal infection, as such, a descriptive review reporting the characteristics of those presenting with spinal infection was carried out. In our review, spinal infection was common in those who had conditions associated with immunosuppression. Additionally, the most frequently reported clinical feature was the classic triad of spinal pain, fever and neurological dysfunction.This is an Open Access article distributed in accordance with the Creative Commons Attribution Non-Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/

Introduction -Exercise, and in particular balance and coordination related activities such as dance, appear to have positive effects on cognitive function, as well as neurodegenerative conditions such as dementia and Parkinson's disease. Quadrupedal gait training is a movement system requiring coordination of all four limbs that has previously been associated with cognitive development in children. There is currently little research into the effect of complex QDP movements on cognitive function in adults.Purpose -To determine the effects of a novel four-week quadrupedal gait training programme on markers of cognitive function and joint reposition sense in healthy adults.Methods -Twenty-two physically active sports science students (15 male and 7 female) were divided into two groups: a training group (TG) and a control group (CG). All participants completed the Wisconsin Card Sorting Task (WCST) and were tested for joint reposition sense before and after a four-week intervention, during which time the TG completed a series of progressive and challenging quadrupedal movement training sessions.Results -Participants in the TG showed significant improvements in the WCST, with improvements in perseverative errors, non-perseverative errors, and conceptual level response. This improvement was not found in the CG. Joint reposition sense also improved for the TG, but only at 20 degrees of shoulder flexion.

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