University of Aberdeen

About: University of Aberdeen is a education organization based out in Aberdeen, United Kingdom. It is known for research contribution in the topics: Population & Randomized controlled trial. The organization has 21174 authors who have published 49962 publications receiving 2105479 citations. The organization is also known as: Aberdeen University.

Prediction models for diagnosis and prognosis of covid-19: systematic review and critical appraisal

Abstract: Objective To review and appraise the validity and usefulness of published and preprint reports of prediction models for diagnosing coronavirus disease 2019 (covid-19) in patients with suspected infection, for prognosis of patients with covid-19, and for detecting people in the general population at increased risk of covid-19 infection or being admitted to hospital with the disease. Design Living systematic review and critical appraisal by the COVID-PRECISE (Precise Risk Estimation to optimise covid-19 Care for Infected or Suspected patients in diverse sEttings) group. Data sources PubMed and Embase through Ovid, up to 1 July 2020, supplemented with arXiv, medRxiv, and bioRxiv up to 5 May 2020. Study selection Studies that developed or validated a multivariable covid-19 related prediction model. Data extraction At least two authors independently extracted data using the CHARMS (critical appraisal and data extraction for systematic reviews of prediction modelling studies) checklist; risk of bias was assessed using PROBAST (prediction model risk of bias assessment tool). Results 37 421 titles were screened, and 169 studies describing 232 prediction models were included. The review identified seven models for identifying people at risk in the general population; 118 diagnostic models for detecting covid-19 (75 were based on medical imaging, 10 to diagnose disease severity); and 107 prognostic models for predicting mortality risk, progression to severe disease, intensive care unit admission, ventilation, intubation, or length of hospital stay. The most frequent types of predictors included in the covid-19 prediction models are vital signs, age, comorbidities, and image features. Flu-like symptoms are frequently predictive in diagnostic models, while sex, C reactive protein, and lymphocyte counts are frequent prognostic factors. Reported C index estimates from the strongest form of validation available per model ranged from 0.71 to 0.99 in prediction models for the general population, from 0.65 to more than 0.99 in diagnostic models, and from 0.54 to 0.99 in prognostic models. All models were rated at high or unclear risk of bias, mostly because of non-representative selection of control patients, exclusion of patients who had not experienced the event of interest by the end of the study, high risk of model overfitting, and unclear reporting. Many models did not include a description of the target population (n=27, 12%) or care setting (n=75, 32%), and only 11 (5%) were externally validated by a calibration plot. The Jehi diagnostic model and the 4C mortality score were identified as promising models. Conclusion Prediction models for covid-19 are quickly entering the academic literature to support medical decision making at a time when they are urgently needed. This review indicates that almost all pubished prediction models are poorly reported, and at high risk of bias such that their reported predictive performance is probably optimistic. However, we have identified two (one diagnostic and one prognostic) promising models that should soon be validated in multiple cohorts, preferably through collaborative efforts and data sharing to also allow an investigation of the stability and heterogeneity in their performance across populations and settings. Details on all reviewed models are publicly available at https://www.covprecise.org/. Methodological guidance as provided in this paper should be followed because unreliable predictions could cause more harm than benefit in guiding clinical decisions. Finally, prediction model authors should adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guideline. Systematic review registration Protocol https://osf.io/ehc47/, registration https://osf.io/wy245. Readers’ note This article is a living systematic review that will be updated to reflect emerging evidence. Updates may occur for up to two years from the date of original publication. This version is update 3 of the original article published on 7 April 2020 (BMJ 2020;369:m1328). Previous updates can be found as data supplements (https://www.bmj.com/content/369/bmj.m1328/related#datasupp). When citing this paper please consider adding the update number and date of access for clarity.

The bottlenose dolphin community of Doubtful Sound features a large proportion of long-lasting associations

Abstract: More than 12 studies of different bottlenose dolphin populations, spanning from tropical to cold temperate waters, have shown that the species typically lives in societies in which relationships among individuals are predominantly fluid. In all cases dolphins lived in small groups characterised by fluid and dynamic interactions and some degree of dispersal from the natal group by both sexes. We describe a small, closed population of bottlenose dolphins living at the southern extreme of the species' range. Individuals live in large, mixed-sex groups in which no permanent emigration/immigration has been observed over the past 7 years. All members within the community are relatively closely associated (average half-weight index>0.4). Both male–male and female–female networks of preferred associates are present, as are long-lasting associations across sexes. The community structure is temporally stable, compared to other bottlenose dolphin populations, and constant companionship seems to be prevalent in the temporal association pattern. Such high degrees of stability are unprecedented in studies of bottlenose dolphins and may be related to the ecological constraints of Doubtful Sound. Fjords are low-productivity systems in which survival may easily require a greater level of co-operation, and hence group stability. These conditions are also present in other cetacean populations forming stable groups. We therefore hypothesise that ecological constraints are important factors shaping social interactions within cetacean societies.

Closing the gap between research and practice: an overview of systematic reviews of interventions to promote the implementation of research findings

Abstract: This is the seventh in a series of eight articles analysing the gap between research and practice Series editors: Andrew Haines and Anna Donald Despite the considerable amount of money spent on clinical research relatively little attention has been paid to ensuring that the findings of research are implemented in routine clinical practice.1 There are many different types of intervention that can be used to promote behavioural change among healthcare professionals and the implementation of research findings. Disentangling the effects of intervention from the influence of contextual factors is difficult when interpreting the results of individual trials of behavioural change.2 Nevertheless, systematic reviews of rigorous studies provide the best evidence of the effectiveness of different strategies for promoting behavioural change. 3 4 In this paper we examine systematic reviews of different strategies for the dissemination and implementation of research findings to identify evidence of the effectiveness of different strategies and to assess the quality of the systematic reviews. #### Summary points Systematic reviews of rigorous studies provide the best evidence on the effectiveness of different strategies to promote the implementation of research findings Passive dissemination of information is generally ineffective It seems necessary to use specific strategies to encourage implementation of research based recommendations and to ensure changes in practice Further research on the relative effectiveness and efficiency of different strategies is required We searched Medline records dating from 1966 to June 1995 using a strategy developed in collaboration with the NHS Centre for Reviews and Dissemination. The search identified 1139 references. No reviews from the Cochrane Effective Practice and Organisation of Care Review Group4 had been published during this time. In addition, we searched the Database of Abstracts of Research Effectiveness (DARE) (http://www.york.ac.uk/inst/crd) but did not identify any other review meeting the inclusion criteria. We searched for any review …

Genetic relationship between five psychiatric disorders estimated from genome-wide SNPs

Abstract: Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohn's disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.