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      A taxonomy has been developed for outcomes in medical research to help improve knowledge discovery

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          Abstract

          Objectives

          There is increasing recognition that insufficient attention has been paid to the choice of outcomes measured in clinical trials. The lack of a standardized outcome classification system results in inconsistencies due to ambiguity and variation in how outcomes are described across different studies. Being able to classify by outcome would increase efficiency in searching sources such as clinical trial registries, patient registries, the Cochrane Database of Systematic Reviews, and the Core Outcome Measures in Effectiveness Trials (COMET) database of core outcome sets (COS), thus aiding knowledge discovery.

          Study Design and Setting

          A literature review was carried out to determine existing outcome classification systems, none of which were sufficiently comprehensive or granular for classification of all potential outcomes from clinical trials. A new taxonomy for outcome classification was developed, and as proof of principle, outcomes extracted from all published COS in the COMET database, selected Cochrane reviews, and clinical trial registry entries were classified using this new system.

          Results

          Application of this new taxonomy to COS in the COMET database revealed that 274/299 (92%) COS include at least one physiological outcome, whereas only 177 (59%) include at least one measure of impact (global quality of life or some measure of functioning) and only 105 (35%) made reference to adverse events.

          Conclusions

          This outcome taxonomy will be used to annotate outcomes included in COS within the COMET database and is currently being piloted for use in Cochrane Reviews within the Cochrane Linked Data Project. Wider implementation of this standard taxonomy in trial and systematic review databases and registries will further promote efficient searching, reporting, and classification of trial outcomes.

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          Most cited references 22

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          Linking clinical variables with health-related quality of life. A conceptual model of patient outcomes.

          Our model proposes a taxonomy or classification scheme for different measures of health outcome. We divide these outcomes into five levels: biological and physiological factors, symptoms, functioning, general health perceptions, and overall quality of life. In addition to classifying these outcome measures, we propose specific causal relationships between them that link traditional clinical variables to measures of HRQL. As one moves from left to right in the model, one moves outward from the cell to the individual to the interaction of the individual as a member of society. The concepts at each level are increasingly integrated and increasingly difficult to define and measure. AT each level, there are an increasing number of inputs that cannot be controlled by clinicians or the health care system as it is traditionally defined.
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            The Human Phenotype Ontology: a tool for annotating and analyzing human hereditary disease.

            There are many thousands of hereditary diseases in humans, each of which has a specific combination of phenotypic features, but computational analysis of phenotypic data has been hampered by lack of adequate computational data structures. Therefore, we have developed a Human Phenotype Ontology (HPO) with over 8000 terms representing individual phenotypic anomalies and have annotated all clinical entries in Online Mendelian Inheritance in Man with the terms of the HPO. We show that the HPO is able to capture phenotypic similarities between diseases in a useful and highly significant fashion.
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              The COMET Handbook: version 1.0

              The selection of appropriate outcomes is crucial when designing clinical trials in order to compare the effects of different interventions directly. For the findings to influence policy and practice, the outcomes need to be relevant and important to key stakeholders including patients and the public, health care professionals and others making decisions about health care. It is now widely acknowledged that insufficient attention has been paid to the choice of outcomes measured in clinical trials. Researchers are increasingly addressing this issue through the development and use of a core outcome set, an agreed standardised collection of outcomes which should be measured and reported, as a minimum, in all trials for a specific clinical area. Accumulating work in this area has identified the need for guidance on the development, implementation, evaluation and updating of core outcome sets. This Handbook, developed by the COMET Initiative, brings together current thinking and methodological research regarding those issues. We recommend a four-step process to develop a core outcome set. The aim is to update the contents of the Handbook as further research is identified. Electronic supplementary material The online version of this article (doi:10.1186/s13063-017-1978-4) contains supplementary material, which is available to authorized users.
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                Author and article information

                Contributors
                Journal
                J Clin Epidemiol
                J Clin Epidemiol
                Journal of Clinical Epidemiology
                Elsevier
                0895-4356
                1878-5921
                1 April 2018
                April 2018
                : 96
                : 84-92
                Affiliations
                [a ]MRC North West Hub for Trials Methodology Research, Department of Biostatistics, Institute of Translational Medicine, University of Liverpool, Liverpool L69 3GS, UK
                [b ]School of Medicine, Dentistry and Biomedical Sciences, Centre for Public Health Institute for Health Sciences, Northern Ireland Methodology Hub, Queen's University Belfast, Belfast, UK
                [c ]Department of Family Medicine, SUNY Upstate Medical University, Syracuse, NY, USA
                [d ]Department of Informatics and Knowledge Management, Cochrane Central Executive, Freiburg, Germany
                [e ]Division of Molecular and Clinical Cancer Sciences, University of Manchester, Manchester, UK
                Author notes
                []Corresponding author. Department of Biostatistics, University of Liverpool, Block F Waterhouse Building, 1-5 Brownlow Street, Liverpool, L63 3GL, UK. Tel.: 0151 794 9758; fax: 0151 795 8770.Department of BiostatisticsUniversity of LiverpoolBlock F Waterhouse Building1-5 Brownlow StreetLiverpoolL63 3GLUK prw@ 123456liverpool.ac.uk
                Article
                S0895-4356(17)30589-9
                10.1016/j.jclinepi.2017.12.020
                5854263
                29288712
                © 2018 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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