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\"I think you're making history, Gene!\" So said Nobel laureate and molecular biologist Joshua Lederberg to his friend Eugene Garfield in 1962. They were building the Science Citation Index (SCI), now the Clarivate Analytics Web of Science, with long-sought grants from US funding agencies. Today, we cannot imagine research without indexes that reveal how articles are cited. Garfield enabled an entire field: scientometrics, the quantitative study of science and technology.

Informative journal abstracts are crucial for the identification and initial appraisal of studies. We aimed to evaluate the informativeness of abstracts of diagnostic accuracy studies. PubMed was searched for reports of studies that had evaluated the diagnostic accuracy of a test against a clinical reference standard, published in 12 high-impact journals in 2012. Two reviewers independently evaluated the information contained in included abstracts using 21 items deemed important based on published guidance for adequate reporting and study quality assessment. We included 103 abstracts. Crucial information on study population, setting, patient sampling, and blinding as well as confidence intervals around accuracy estimates were reported in <50% of the abstracts. The mean number of reported items per abstract was 10.1 of 21 (standard deviation 2.2). The mean number of reported items was significantly lower for multiple-gate (case–control type) studies, in reports in specialty journals, and for studies with smaller sample sizes and lower abstract word counts. No significant differences were found between studies evaluating different types of tests. Many abstracts of diagnostic accuracy study reports in high-impact journals are insufficiently informative. Developing guidelines for such abstracts could help the transparency and completeness of reporting.

Standards for evaluating evidence-based medicine (EBM) point-of-care (POC) summaries of research are lacking. The authors developed a \"Critical Appraisal for Summaries of Evidence\" (CASE) worksheet to help assess the evidence in these tools. The authors then evaluated the reliability of the worksheet. The CASE worksheet was developed with 10 questions covering specificity, authorship, reviewers, methods, grading, clarity, citations, currency, bias, and relevancy. Two reviewers independently assessed a random selection of 384 EBM POC summaries using the worksheet. The responses of the raters were then compared using a kappa score. The kappa statistic demonstrated an overall moderate agreement (κ = 0.44) between the reviewers using the CASE worksheet for the 384 summaries. The 3 categories of evaluation questions in which the reviewers disagreed most often were citations (κ =  0), bias (κ = 0.11), and currency (κ = -0.18). The CASE worksheet provided an effective checklist for critically analyzing a treatment summary. While the reviewers agreed on worksheet responses for most questions, variation occurred in how the raters navigated the tool and interpreted some of the questions. Further validation of the form by other groups of users should be investigated.

Elaine Beller and colleagues from the PRISMA for Abstracts group provide a reporting guidelines for reporting abstracts of systematic reviews in journals and at conferences.Elaine Beller and colleagues from the PRISMA for Abstracts group provide a reporting guidelines for reporting abstracts of systematic reviews in journals and at conferences.

Background This article provides an overview of the first BioASQ challenge, a competition on large-scale biomedical semantic indexing and question answering ( QA ), which took place between March and September 2013. BioASQ assesses the ability of systems to semantically index very large numbers of biomedical scientific articles, and to return concise and user-understandable answers to given natural language questions by combining information from biomedical articles and ontologies. Results The 2013 BioASQ competition comprised two tasks, Task 1a and Task 1b. In Task 1a participants were asked to automatically annotate new PubMed documents with MeSH headings. Twelve teams participated in Task 1a, with a total of 46 system runs submitted, and one of the teams performing consistently better than the MTI indexer used by NLM to suggest MeSH headings to curators. Task 1b used benchmark datasets containing 29 development and 282 test English questions, along with gold standard (reference) answers, prepared by a team of biomedical experts from around Europe and participants had to automatically produce answers. Three teams participated in Task 1b, with 11 system runs. The BioASQ infrastructure, including benchmark datasets, evaluation mechanisms, and the results of the participants and baseline methods, is publicly available. Conclusions A publicly available evaluation infrastructure for biomedical semantic indexing and QA has been developed, which includes benchmark datasets, and can be used to evaluate systems that: assign MeSH headings to published articles or to English questions; retrieve relevant RDF triples from ontologies, relevant articles and snippets from PubMed Central; produce “exact” and paragraph-sized “ideal” answers (summaries). The results of the systems that participated in the 2013 BioASQ competition are promising. In Task 1a one of the systems performed consistently better from the NLM ’s MTI indexer. In Task 1b the systems received high scores in the manual evaluation of the “ideal” answers; hence, they produced high quality summaries as answers. Overall, BioASQ helped obtain a unified view of how techniques from text classification, semantic indexing, document and passage retrieval, question answering, and text summarization can be combined to allow biomedical experts to obtain concise, user-understandable answers to questions reflecting their real information needs.

Objective To investigate whether language used in science abstracts can skew towards the use of strikingly positive and negative words over time.Design Retrospective analysis of all scientific abstracts in PubMed between 1974 and 2014.Methods The yearly frequencies of positive, negative, and neutral words (25 preselected words in each category), plus 100 randomly selected words were normalised for the total number of abstracts. Subanalyses included pattern quantification of individual words, specificity for selected high impact journals, and comparison between author affiliations within or outside countries with English as the official majority language. Frequency patterns were compared with 4% of all books ever printed and digitised by use of Google Books Ngram Viewer. Main outcome measures Frequencies of positive and negative words in abstracts compared with frequencies of words with a neutral and random connotation, expressed as relative change since 1980.Results The absolute frequency of positive words increased from 2.0% (1974-80) to 17.5% (2014), a relative increase of 880% over four decades. All 25 individual positive words contributed to the increase, particularly the words “robust,” “novel,” “innovative,” and “unprecedented,” which increased in relative frequency up to 15 000%. Comparable but less pronounced results were obtained when restricting the analysis to selected journals with high impact factors. Authors affiliated to an institute in a non-English speaking country used significantly more positive words. Negative word frequencies increased from 1.3% (1974-80) to 3.2% (2014), a relative increase of 257%. Over the same time period, no apparent increase was found in neutral or random word use, or in the frequency of positive word use in published books.Conclusions Our lexicographic analysis indicates that scientific abstracts are currently written with more positive and negative words, and provides an insight into the evolution of scientific writing. Apparently scientists look on the bright side of research results. But whether this perception fits reality should be questioned.

[...]Elsevier recently sug- gested that papers be accompanied by a five-minute audio presenta- tion in which the authors describe their studies and outcomes in their own words. [...]I note three problems with the current research that I also reported ten years ago: 1.

Checklist items (table)7 for reporting an abstract of a randomised trial include: details of the trial's objectives; trial design (eg, method of allocation, blinding); participants in the trial (ie, description, numbers randomised and analysed); interventions intended for each randomised group and their effect on primary efficacy outcomes and harms; the trial's conclusions; the trial's registration name and number; and source of funding.

The Rochester Epidemiology Project (REP) has maintained a comprehensive medical records linkage system for nearly half a century for almost all persons residing in Olmsted County, Minnesota. Herein, we provide a brief history of the REP before and after 1966, the year in which the REP was officially established. The key protagonists before 1966 were Henry Plummer, Mabel Root, and Joseph Berkson, who developed a medical records linkage system at Mayo Clinic. In 1966, Leonard Kurland established collaborative agreements with other local health care providers (hospitals, physician groups, and clinics [primarily Olmsted Medical Center]) to develop a medical records linkage system that covered the entire population of Olmsted County, and he obtained funding from the National Institutes of Health to support the new system. In 1997, L. Joseph Melton III addressed emerging concerns about the confidentiality of medical record information by introducing a broad patient research authorization as per Minnesota state law. We describe how the key protagonists of the REP have responded to challenges posed by evolving medical knowledge, information technology, and public expectation and policy. In addition, we provide a general description of the system; discuss issues of data quality, reliability, and validity; describe the research team structure; provide information about funding; and compare the REP with other medical information systems. The REP can serve as a model for the development of similar research infrastructures in the United States and worldwide.

Abstract Objective To compare the distribution of P values in abstracts of randomised controlled trials with that in observational studies, and to check P values between 0.04 and 0.06. Design Cross sectional study of all 260 abstracts in PubMed of articles published in 2003 that contained “relative risk” or “odds ratio” and reported results from a randomised trial, and random samples of 130 abstracts from cohort studies and 130 from case-control studies. P values were noted or calculated if unreported. Main outcome measures Prevalence of significant P values in abstracts and distribution of P values between 0.04 and 0.06. Results The first result in the abstract was statistically significant in 70% of the trials, 84% of cohort studies, and 84% of case-control studies. Although many of these results were derived from subgroup or secondary analyses, or biased selection of results, they were presented without reservations in 98% of the trials. P values were more extreme in observational studies (P < 0.001) and in cohort studies than in case-control studies (P = 0.04). The distribution of P values around P = 0.05 was extremely skewed. Only five trials had 0.05 ≤ P < 0.06, whereas 29 trials had 0.04 ≤ P < 0.05. I could check the calculations for 27 of these trials. One of four non-significant results was significant. Four of the 23 significant results were wrong, five were doubtful, and four could be discussed. Nine cohort studies and eight case-control studies reported P values between 0.04 and 0.06, but in all 17 cases P < 0.05. Because the analyses had been adjusted for confounders, these results could not be checked. Conclusions Significant results in abstracts are common but should generally be disbelieved.