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About the Authors: A. Cecile J. W. Janssens Roles Conceptualization, Investigation, Methodology, Resources, Supervision, Visualization, Writing - original draft, Writing - review & editing * E-mail: cecile.janssens@emory.edu Affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America Michael Goodman Roles Writing - review & editing Affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of America Kimberly R. Powell Roles Investigation, Methodology, Writing - review & editing Affiliation: Woodruff Health Sciences Center Library, Emory University, Atlanta, Georgia, United States of America Marta Gwinn Roles Writing - review & editing Affiliation: Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, United States of AmericaCitation: Janssens ACJW, Goodman M, Powell KR, Gwinn M (2017) A critical evaluation of the algorithm behind the Relative Citation Ratio (RCR). Abbreviations: ACR, article citation rate; FCR, field citation rate; JCR, journal citation rate; JIF, journal impact factor; NIH, National Institutes of Health; RCR, Relative Citation Ratio Provenance: Not commissioned; externally peer reviewed. Research articles generally lose their relevance when science progresses, and after a while, they may no longer be cited [6]. Because the number of years since publication continues to increase, the ACR will inevitably decline, and so will the RCR. [...]the RCR is normalized against a collection of 311,497 publications from NIH projects to obtain a benchmark against which articles can be compared.

In ethical and regulatory discussions on new applications of genomic testing technologies, the notion of ‘personal utility’ has been mentioned repeatedly. It has been used to justify direct access to commercially offered genomic testing or feedback of individual research results to research or biobank participants. Sometimes research participants or consumers claim a right to genomic information with an appeal to personal utility. As of yet, no systematic account of the umbrella notion of personal utility has been given. This paper offers a definition of personal utility that places it in the middle of the spectrum between clinical utility and personal perceptions of utility, and that acknowledges its normative charge. The paper discusses two perspectives on personal utility, the healthcare perspective and the consumer perspective, and argues that these are too narrow and too wide, respectively. Instead, it proposes a normative definition of personal utility that postulates information and potential use as necessary conditions of utility. This definition entails that perceived utility does not equal personal utility, and that expert judgment may be necessary to help determine whether a genomic test can have personal utility for someone. Two examples of genomic tests are presented to illustrate the discrepancies between perceived utility and our proposed definition of personal utility. The paper concludes that while there is room for the notion of personal utility in the ethical evaluation and regulation of genomic tests, the justificatory role of personal utility is not unlimited. For in the absence of clinical validity and reasonable potential use of information, there is no personal utility.

A study of 3,500 children in the UK shows that data on socioeconomic background and previous educational achievements can better predict how students will perform at school than genetic data.A study of 3,500 children in the UK shows that data on socioeconomic background and previous educational achievements can better predict how students will perform at school than genetic data.

While the authors agree with John Ioannidis that \"most research findings are false,\" here they show that replication of research findings enhances the positive predictive value of research findings being true.

In recent studies, this increased risk is claimed to be similar to conditions associated with known causal genetic mutations. [...]the argument goes, it is time to start thinking about implementing PRSs in routine clinical care (1, 2). [...]another study (5) also investigated polygenic risk prediction of CAD using 1.7 million SNPs in the UK Biobank and found a similar high AUC (0.79) for the prediction ofprevalent cases that included age but a low AUC (0.62) for the prediction of incident cases in which age was used as the time variable. [...]the authors concluded that people at high polygenic risk would not have been identified using clinical risk factors because they had similar means and frequencies of clinical risk factors to the rest. Moving Forward The increasing interest in the study of PRSs warrants a reflection on what evidence is needed to move the scores to healthcare practice, if proven effective. Because the predictive ability of risk models is known to vary between populations, it is crucial that a prediction analysis be conducted in a population that is representative for the population in which the PRS is intended to be applied:

Background We recently developed CoCites, a citation-based search method that is designed to be more efficient than traditional keyword-based methods. The method begins with identification of one or more highly relevant publications (query articles) and consists of two searches: the co-citation search, which ranks publications on their co-citation frequency with the query articles, and the citation search, which ranks publications on frequency of all citations that cite or are cited by the query articles. Methods We aimed to reproduce the literature searches of published systematic reviews and meta-analyses and assess whether CoCites retrieves all eligible articles while screening fewer titles. Results A total of 250 reviews were included. CoCites retrieved a median of 75% of the articles that were included in the original reviews. The percentage of retrieved articles was higher (88%) when the query articles were cited more frequently and when they had more overlap in their citations. Applying CoCites to only the highest-cited article yielded similar results. The co-citation and citation searches combined were more efficient when the review authors had screened more than 500 titles, but not when they had screened less. Conclusions CoCites is an efficient and accurate method for finding relevant related articles. The method uses the expert knowledge of authors to rank related articles, does not depend on keyword selection and requires no special expertise to build search queries. The method is transparent and reproducible.

An Essay by A. Cecile Janssens and Peter Kraft discusses the limitations inherent in research involving collection of self-reported data by self-selected participants, and makes proposals for upfront communication of such limitations to study participants.

The rapid identification of genetic markers for multifactorial diseases from genome-wide association studies is fuelling interest in investigating the predictive ability and health care utility of genetic risk models. Various measures are available for the assessment of risk prediction models, each addressing a different aspect of performance and utility. We developed PredictABEL, a package in R that covers descriptive tables, measures and figures that are used in the analysis of risk prediction studies such as measures of model fit, predictive ability and clinical utility, and risk distributions, calibration plot and the receiver operating characteristic plot. Tables and figures are saved as separate files in a user-specified format, which include publicationquality EPS and TIFF formats. All figures are available in a ready-made layout, but they can be customized to the preferences of the user. The package has been developed for the analysis of genetic risk prediction studies, but can also be used for studies that only include non-genetic risk factors. PredictABEL is freely available at the websites of GenABEL (http://www. genabel. org) and CRAN (http://cran. r-project. org/).

In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.In a Policy Forum, Muin Khoury and colleagues discuss research on the clinical application of genome sequencing data.

Experts may not need to see the ROC plot as they can visualize risk distributions in their heads knowing the value of the AUC, the confidence interval, the predictors, the incidence of disease, and the sample size. The ROC plot presents one curve per prediction model, the classification plot two. Besides discriminative ability, the ROC plot and classification plot also hint about the sample size, the percentage of cases, or the number of different risk estimates. Prediction models typically aim to identify patients whose increased risks warrant treatment or intervention and this requires setting a risk threshold. [...]when adding a risk factor improves the discriminative ability of a prediction model, the estimated risks of individuals at the high-risk end of the distribution increase.