Dataset Information

From abstract to impact in cardiovascular research: factors predicting publication and citation.

ABSTRACT:

Aims

Through a 4-year follow-up of the abstracts submitted to the European Society of Cardiology Congress in 2006, we aimed at identifying factors predicting high-quality research, appraising the quality of the peer review and editorial processes, and thereby revealing potential ways to improve future research, peer review, and editorial work. METHODS AND RESULTS All abstracts submitted in 2006 were assessed for acceptance, presentation format, and average reviewer rating. Accepted and rejected studies were followed for 4 years. Multivariate regression analyses of a representative selection of 10% of all abstracts (n= 1002) were performed to identify factors predicting acceptance, subsequent publication, and citation. A total of 10 020 abstracts were submitted, 3104 (31%) were accepted for poster, and 701 (7%) for oral presentation. At Congress level, basic research, a patient number ≥ 100, and prospective study design were identified as independent predictors of acceptance. These factors differed from those predicting full-text publication, which included academic affiliation. The single parameter predicting frequent citation was study design with randomized controlled trials reaching the highest citation rates. The publication rate of accepted studies was 38%, whereas only 24% of rejected studies were published. Among published studies, those accepted at the Congress received higher citation rates than rejected ones.

Conclusions

Research of high quality was determined by study design and largely identified at Congress level through blinded peer review. The scientometric follow-up revealed a marked disparity between predictors of full-text publication and those predicting citation or acceptance at the Congress.

SUBMITTER: Winnik S

PROVIDER: S-EPMC3530902 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPublication records and citation indices often are used to evaluate academic performance. For this reason, obtaining or computing them accurately is important. This can be difficult, largely due to a lack of complete knowledge of an individual's publication list and/or lack of time available to manually obtain or construct the publication-citation record. While online publication search engines have somewhat addressed these problems, using raw search results can yield inaccurate estimates of publication-citation records and citation indices.MethodologyIn this paper, we present a new, automated method that produces estimates of an individual's publication-citation record from an individual's name and a set of domain-specific vocabulary that may occur in the individual's publication titles. Because this vocabulary can be harvested directly from a research web page or online (partial) publication list, our method delivers an easy way to obtain estimates of a publication-citation record and the relevant citation indices. Our method works by applying a series of stringent name and content filters to the raw publication search results returned by an online publication search engine. In this paper, our method is run using Google Scholar, but the underlying filters can be easily applied to any existing publication search engine. When compared against a manually constructed data set of individuals and their publication-citation records, our method provides significant improvements over raw search results. The estimated publication-citation records returned by our method have an average sensitivity of 98% and specificity of 72% (in contrast to raw search result specificity of less than 10%). When citation indices are computed using these records, the estimated indices are within of the true value 10%, compared to raw search results which have overestimates of, on average, 75%.ConclusionsThese results confirm that our method provides significantly improved estimates over raw search results, and these can either be used directly for large-scale (departmental or university) analysis or further refined manually to quickly give accurate publication-citation records.

Project description:BackgroundSystematic reviews are important for informing clinical practice and health policy. The aim of this study was to examine the bibliometrics of systematic reviews and to determine the amount of variance in citations predicted by the journal impact factor (JIF) alone and combined with several other characteristics.MethodsWe conducted a bibliometric analysis of 1,261 systematic reviews published in 2008 and the citations to them in the Scopus database from 2008 to June 2012. Potential predictors of the citation impact of the reviews were examined using descriptive, univariate and multiple regression analysis.ResultsThe mean number of citations per review over four years was 26.5 (SD ± 29.9) or 6.6 citations per review per year. The mean JIF of the journals in which the reviews were published was 4.3 (SD ± 4.2). We found that 17% of the reviews accounted for 50% of the total citations and 1.6% of the reviews were not cited. The number of authors was correlated with the number of citations (r = 0.215, P < 0.001). Higher numbers of citations were associated with the following characteristics: first author from the United States (36.5 citations), an ICD-10 chapter heading of Neoplasms (31.8 citations), type of intervention classified as Investigation, Diagnostics or Screening (34.7 citations) and having an international collaboration (32.1 citations). The JIF alone explained more than half of the variation in citations (R(2) = 0.59) in univariate analysis. Adjusting for both JIF and type of intervention increased the R2 value to 0.81. Fourteen percent of reviews published in the top quartile of JIFs (? 5.16) received citations in the bottom quartile (eight or fewer), whereas 9% of reviews published in the lowest JIF quartile (? 2.06) received citations in the top quartile (34 or more). Six percent of reviews in journals with no JIF were also in the first quartile of citations.ConclusionsThe JIF predicted over half of the variation in citations to the systematic reviews. However, the distribution of citations was markedly skewed. Some reviews in journals with low JIFs were well-cited and others in higher JIF journals received relatively few citations; hence the JIF did not accurately represent the number of citations to individual systematic reviews.

Dataset Information

From abstract to impact in cardiovascular research: factors predicting publication and citation.

Aims

Conclusions

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