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In this New York State registry study, outcomes of patients with multivessel coronary disease treated with drug-eluting coronary stents or coronary-artery bypass grafting (CABG) were compared during 18 months of follow-up. The rates of death, death or myocardial infarction, and repeat revascularization were consistently lower after CABG than after treatment with drug-eluting stents. The rates of death, death or myocardial infarction, and repeat revascularization were consistently lower after CABG than after treatment with drug-eluting stents. Several studies have compared the long-term outcomes of coronary-artery bypass grafting (CABG) and coronary stenting. 1 – 5 In 2003, drug-eluting stents were introduced for the purpose of reducing restenosis, which has continued to be a problem associated with the use of bare-metal stents. Many randomized, controlled trials have documented lower rates of clinical and angiographic restenosis, target-lesion revascularization, and major adverse cardiac events with drug-eluting stents. 6 – 20 However, recent reports of the danger of late stent thrombosis among patients with drug-eluting stents 21 , 22 led to a meeting of a Food and Drug Administration (FDA) advisory committee that addressed the safety of . . .

Using data derived from two large New York State registries, this study compared three-year survival rates among patients with multivessel coronary disease who had undergone coronary-artery bypass grafting (CABG) and those who had received a coronary stent. Survival rates were uniformly superior with CABG. Although this study was not randomized, it has important implications for the selection of revascularization procedures in patients with multivessel coronary disease. This study compared three-year survival rates among patients with multivessel coronary disease who had undergone coronary-artery bypass grafting (CABG) and those who had received a coronary stent. Coronary-artery bypass grafting (CABG) and percutaneous coronary intervention (PCI) have long been the definitive aggressive options for treating patients with coronary artery disease. In the past few years, several randomized clinical trials 1 – 15 and observational studies 16 – 20 have examined the relative long-term benefits of these interventions. However, with few exceptions, these studies were conducted before the availability of stenting. 8 , 11 , 13 , 15 We used observational data from a very large registry to compare short-term and long-term outcomes among patients with multivessel disease who underwent CABG or stenting in New York State hospitals. Methods Databases The two main databases used in . . .

Purpose To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. Method Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of performance on step 1 and step 2. A generalized regression neural network (GRNN) as well as a feed forward neural network (FFNN) was then developed in an effort to more accurately predict step 1 and step 2 scores from these preadmission data. Results Statistically significant predictors for step 1 and step 2 included science grade point average (SGPA), the biologic science (BS) section of the Medical College Admissions Test (MCAT), college selectivity, race, and age of the applicant. Neural networks were found to predict a significant portion of the variance, and the FFNN demonstrated some superiority over that obtained with linear regression models as well as the GRNN. Conclusions The results have implications that could impact the selection of applicants to medical school and the neural networks that we developed could be used in a prospective manner.

The transition from a delivery model based on volume to a model based on value is underpinning an unprecedented rate of change in healthcare. Our research identifies academic medical centers that have successfully moved their organizations from a volume-based care delivery model to a model that is based on value, achieving top decile performance in the transition. We asked senior leaders about their key imperatives in achieving system-wide healthcare transformations.Major teaching hospitals were sampled using data envelopment analysis to identify hospitals that produced the best value-based purchasing process of care, Hospital Consumer Assessment of Healthcare Providers and Systems scores, and mortality scores with the most efficient utilization of human and financial organizational resources. On-site interviews were conducted with the CEOs of the top-performing hospitals. Content analysis of the transcribed interviews revealed 10 clear imperatives that our sample of CEOs said were essential.

We examined whether we could develop models based on data provided to the United States Renal Data System (USRDS) to accurately predict survival. Records were obtained from patients beginning dialysis in 1990 through 2007. We developed linear and neural network models and optimized the fit of these models to the actual time to death. Next, we examined whether we could accurately predict survival in a dataset containing censored and uncensored patients. The results with these models were contrasted with those obtained with a Cox proportional hazards model fit to the entire dataset. The average C statistic over a 6-month to 10-year time range achieved with these models was approximately 0.7891 (linear model), 0.7804 (transformed dataset linear model), 0.7769 (neural network model), 0.7774 (transformed dataset neural network model), 0.8019 (Cox model), and 0.7970 (transformed dataset Cox model). When we used the Cox proportional hazards model, superior C statistic results were found at time points between 2 and 10 years but at earlier time points, the Cox model was slightly inferior. These results suggest that data provided to the USRDS can allow for predictive models which have a high degree of accuracy years following the initiation of dialysis. Copyright © 2010 S. Karger AG, Basel [PUBLICATION ABSTRACT]

\"This is tragic for all concerned,\" said Topps chief operating officer Anthony D'Urso, a member of the family that founded the company in 1940. Topps conceded that much of the recalled meat had already been eaten, and on Friday expressed regret that its product had been linked to illnesses. \"We hope and pray for the full recovery of those individuals,\" D'Urso said in a statement. The recall represents all Topps hamburger products with either a \"sell by date\" or a \"best if used by date\" between Sept. 25, 2007 and Sept. 25, 2008. All recalled products also have the USDA establishment number EST 9748, which is on the back panel of the package or in the USDA legend. A full list of the recalled products is available at http://www.toppsmeat.com.

The tests confirmed on Sept. 7 that the Topps patties had the E coli bacteria strain O157:H7, according to an e-mail from Kis Robertson, an employee of the USDA's Food Safety Inspection Service. Privately held Topps, which is believed to be the leading U.S. maker of frozen hamburger patties, said it sells its products to supermarkets and institutions such as schools, hospitals, restaurants and hotels. The recall represents all Topps hamburger products with either a \"sell by date\" or a \"best if used by date\" between Sept. 25 this year and Sept. 25, 2008. All recalled products also have a USDA establishment number of EST 9748, which is on the back panel of the package or in the USDA legend. A full list of the recalled products is available at http://www.toppsmeat.com.

Deval L. Patrick, assistant attorney general for civil rights, said the government switched sides because the judgment in favor of [Sharon Taxman] was wrong. Taxman returned for the 1990-91 school year while another teacher was on maternity leave. After sitting out 1991-92, Taxman has been back the past two years because of retirements. The Justice Department under President [Bush] sued on Taxman's behalf, and U.S. District Judge Maryanne Trump Barry in Newark ruled in her favor.