Search for the Radiative Capture d+d->^4He+\gamma Reaction from the dd\mu Muonic Molecule State

A search for the muon catalyzed fusion reaction dd --> ^4He +\gamma in the dd\mu muonic molecule was performed using the experimental \mu CF installation TRITON and NaI(Tl) detectors for \gamma-quanta. The high pressure target filled with deuterium at temperatures from 85 K to 800 K was exposed to the negative muon beam of the JINR phasotron to detect \gamma-quanta with energy 23.8 MeV. The first experimental estimation for the yield of the radiative deuteron capture from the dd\mu state J=1 was obtained at the level n_{\gamma}\leq 2\times 10^{-5} per one fusion.Comment: 9 pages, 3 Postscript figures, submitted to Phys. At. Nuc

Does adding risk-trends to survival models improve in-hospital mortality predictions? A cohort study

<p>Abstract</p> <p>Background</p> <p>Clinicians informally assess changes in patients' status over time to prognosticate their outcomes. The incorporation of trends in patient status into regression models could improve their ability to predict outcomes. In this study, we used a unique approach to measure trends in patient hospital death risk and determined whether the incorporation of these trend measures into a survival model improved the accuracy of its risk predictions.</p> <p>Methods</p> <p>We included all adult inpatient hospitalizations between 1 April 2004 and 31 March 2009 at our institution. We used the daily mortality risk scores from an existing time-dependent survival model to create five trend indicators: absolute and relative percent change in the risk score from the previous day; absolute and relative percent change in the risk score from the start of the trend; and number of days with a trend in the risk score. In the derivation set, we determined which trend indicators were associated with time to death in hospital, independent of the existing covariates. In the validation set, we compared the predictive performance of the existing model with and without the trend indicators.</p> <p>Results</p> <p>Three trend indicators were independently associated with time to hospital mortality: the absolute change in the risk score from the previous day; the absolute change in the risk score from the start of the trend; and the number of consecutive days with a trend in the risk score. However, adding these trend indicators to the existing model resulted in only small improvements in model discrimination and calibration.</p> <p>Conclusions</p> <p>We produced several indicators of trend in patient risk that were significantly associated with time to hospital death independent of the model used to create them. In other survival models, our approach of incorporating risk trends could be explored to improve their performance without the collection of additional data.</p

The example of CaPSURE: lessons learned from a national disease registry

IntroductionAlthough randomized controlled trials (RCTs) remain the gold standard for determining evidence-based clinical practices, large disease registries that enroll large numbers of patients have become paramount as a relatively cost-effective additional tool.MethodsWe highlight the advantages of disease registries focusing on the example of prostate cancer and the Cancer of the Prostate Strategic Urologic Research Endeavor (CaPSURE™) registry.ResultsCaPSURE collects approximately 1,000 clinical and patient-reported variables, in over 13,000 men that are enrolled. Thus far, CaPSURE has yielded over 130 peer-reviewed publications, with several others in press, in key areas of risk migration, practice patterns, outcome prediction, and quality of life outcomes.ConclusionsDisease registries, like CaPSURE complement RCTs and CaPSURE, have provided a means to better understand many aspects of prostate cancer epidemiology, practice patterns, oncologic and HRQOL outcomes, and costs of care across populations. Specialized observational disease registries such as CaPSURE provide insight and have broad implications for disease management and policy