Author Self-Citation in the General Medicine Literature

Background: Author self-citation contributes to the overall citation count of an article and the impact factor of the journal in which it appears. Little is known, however, about the extent of self-citation in the general clinical medicine literature. The objective of this study was to determine the extent and temporal pattern of author self-citation and the article characteristics associated with author self-citation. Methodology/Principal Findings: We performed a retrospective cohort study of articles published in three high impact general medical journals (JAMA, Lancet, and New England Journal of Medicine) between October 1, 1999 and March 31, 2000. We retrieved the number and percentage of author self-citations received by the article since publication, as of June 2008, from the Scopus citation database. Several article characteristics were extracted by two blinded, independent reviewers for each article in the cohort and analyzed in multivariable linear regression analyses. Since publication, author self-citations accounted for 6.5 % (95 % confidence interval 6.3–6.7%) of all citations received by the 328 articles in our sample. Selfcitation peaked in 2002, declining annually thereafter. Studies with more authors, in cardiovascular medicine or infectious disease, and with smaller sample size were associated with more author self-citations and higher percentage of author selfcitation (all p#0.01). Conclusions/Significance: Approximately 1 in 15 citations of articles in high-profile general medicine journals are autho

Hepatitis C virus genotype frequency in Isfahan province of Iran: a descriptive cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Hepatitis C is an infectious disease affecting the liver, caused by the hepatitis C virus (HCV). The hepatitis C virus is a small, enveloped, single-stranded, positive sense RNA virus with a large genetic heterogeneity. Isolates have been classified into at least eleven major genotypes, based on a nucleotide sequence divergence of 30-35%. Genotypes 1, 2 and 3 circulate around the world, while other genotypes are mainly restricted to determined geographical areas. Genotype determination of HCV is clinically valuable as it provides important information which can be used to determine the type and duration of therapy and to predict the outcome of the disease.</p> <p>Results</p> <p>Plasma samples were collected from ninety seven HCV RNA positive patients admitted to two large medical laboratory centers in Isfahan province (Iran) from the years 2007 to 2009. Samples from patients were subjected to HCV genotype determination using a PCR based genotyping kit. The frequency of HCV genotypes was determined as follows: genotype 3a (61.2%), genotype 1a (29.5%), genotype 1b (5.1%), genotype 2 (2%) and mixed genotypes of 1a+3a (2%).</p> <p>Conclusion</p> <p>Genotype 3a is the most frequent followed by the genotype 1a, genotype 1b and genotype 2 in Isfahan province, Iran.</p

Numerical Model‐Software for Predicting Rock Formation Failure‐Time Using Fracture Mechanics

Real‐time integrated drilling is an important practice for the upstream petroleum industry. Traditional pre‐drill models, tend to offset the data gathered from the field since information obtained prior to spudding and drilling of new wells often become obsolete due to the changes in geology and geomechanics of reservoir‐rocks or formations. Estimating the complicated non‐linear failure‐time of a rock formation is a difficult but important task that helps to mitigate the effects of rock failure when drilling and producing wells from the subsurface. In this study, parameters that have the strongest impact on rock failure were used to develop a numerical and computational model for evaluating wellbore instability in terms of collapse, fracture, rock strength and failure‐time. This approach presents drilling and well engineers with a better understanding of the fracture mechanics and rock strength failureprediction procedure required to reduce stability problems by forecasting the rock/formation failuretime. The computational technique built into the software, uses the stress distribution around a rock formation as well as the rock’s responses to induced stress as a means of analyzing the failure time of the rock. The results from simulation show that the applied stress has the most significant influence on the failure‐time of the rock. The software also shows that the failure‐time varied over several orders of magnitude for varying stress‐loads. Thus, this will help drilling engineers avoid wellbore failure by adjusting the stress concentration properly through altering the mud pressure and well orientation with respect to in‐situ stresses. As observed from the simulation results for the failure time analysis, the trend shows that the time dependent strength failure is not just a function of the applied stress. Because, at applied stress of 6000–6050 psi there was time dependent failure whereas, at higher applied stress of 6350–6400 psi there was no time dependent strength failure