121 research outputs found

    Improving blood pressure control through pharmacist interventions: a meta-analysis of randomized controlled trials.

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    BACKGROUND: Control of blood pressure (BP) remains a major challenge in primary care. Innovative interventions to improve BP control are therefore needed. By updating and combining data from 2 previous systematic reviews, we assess the effect of pharmacist interventions on BP and identify potential determinants of heterogeneity. METHODS AND RESULTS: Randomized controlled trials (RCTs) assessing the effect of pharmacist interventions on BP among outpatients with or without diabetes were identified from MEDLINE, EMBASE, CINAHL, and CENTRAL databases. Weighted mean differences in BP were estimated using random effect models. Prediction intervals (PI) were computed to better express uncertainties in the effect estimates. Thirty-nine RCTs were included with 14 224 patients. Pharmacist interventions mainly included patient education, feedback to physician, and medication management. Compared with usual care, pharmacist interventions showed greater reduction in systolic BP (-7.6 mm Hg, 95% CI: -9.0 to -6.3; I(2)=67%) and diastolic BP (-3.9 mm Hg, 95% CI: -5.1 to -2.8; I(2)=83%). The 95% PI ranged from -13.9 to -1.4 mm Hg for systolic BP and from -9.9 to +2.0 mm Hg for diastolic BP. The effect tended to be larger if the intervention was led by the pharmacist and was done at least monthly. CONCLUSIONS: Pharmacist interventions - alone or in collaboration with other healthcare professionals - improved BP management. Nevertheless, pharmacist interventions had differential effects on BP, from very large to modest or no effect; and determinants of heterogeneity could not be identified. Determining the most efficient, cost-effective, and least time-consuming intervention should be addressed with further research

    Analysis of survival data with cure fraction and variable selection: A pseudo-observations approach

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    In biomedical studies, survival data with a cure fraction (the proportion of subjects cured of disease) are commonly encountered. The mixture cure and bounded cumulative hazard models are two main types of cure fraction models when analyzing survival data with long-term survivors. In this article, in the framework of the Cox proportional hazards mixture cure model and bounded cumulative hazard model, we propose several estimators utilizing pseudo-observations to assess the effects of covariates on the cure rate and the risk of having the event of interest for survival data with a cure fraction. A variable selection procedure is also presented based on the pseudo-observations using penalized generalized estimating equations for proportional hazards mixture cure and bounded cumulative hazard models. Extensive simulation studies are conducted to examine the proposed methods. The proposed technique is demonstrated through applications to a melanoma study and a dental data set with high-dimensional covariates

    A jump-growth model for predator-prey dynamics: derivation and application to marine ecosystems

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    This paper investigates the dynamics of biomass in a marine ecosystem. A stochastic process is defined in which organisms undergo jumps in body size as they catch and eat smaller organisms. Using a systematic expansion of the master equation, we derive a deterministic equation for the macroscopic dynamics, which we call the deterministic jump-growth equation, and a linear Fokker-Planck equation for the stochastic fluctuations. The McKendrick--von Foerster equation, used in previous studies, is shown to be a first-order approximation, appropriate in equilibrium systems where predators are much larger than their prey. The model has a power-law steady state consistent with the approximate constancy of mass density in logarithmic intervals of body mass often observed in marine ecosystems. The behaviours of the stochastic process, the deterministic jump-growth equation and the McKendrick--von Foerster equation are compared using numerical methods. The numerical analysis shows two classes of attractors: steady states and travelling waves.Comment: 27 pages, 4 figures. Final version as published. Only minor change

    Crustal development in the Agnew region, Western Australia, as shown by Rb/Sr isotopic and geochemical studies

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    Rb/Sr geochronology on a folded greenstone-granitoid complex in the Agnew area, Western Australia, yields four distinct ages of igneous activity that conform with stratigraphic and intrusive relationships. They are (using View the MathML source, NBS 70A = 522 ppm Rb and 65.3 ppm Sr): Ma 87Sr/86Sr initial (IR) (1) Differentiated gabbro-granophyre from a stratigraphically old (Kathleen Valley) greenstone sequence > 2718 ± 50 0.7007 ± 0.0004 (2) Voluminous tonalite, the Lawlers Tonalite 2652 ± 20 0.70152 ± 0.00012 2576 ± 14 0.70218 ± 0.00021 (3) A less voluminous leucogranite, and a large complex pegmatite cutting the Perseverance nickel orebody 2588 ± 18 0.7624 ± 0.0068 (4) Aplitic leucotonalite (very minor volumes but widespread) 2474 ± 14 0.70193 ± 0.00012 Table optionsEarlier measurements by Roddick et al. (1976), 70 km to the north, of the Mount Keith Granodiorite (2632 ± 17 Ma, IR = 0.70149 ± 0.00015) and Jones Creek Pegmatites (2481 ± 18 Ma, IR = 0.7253 ± 0.0002) appear to fit groups (2) and (4), respectively. The two principal deformational events in the area are bracketed by groups (2) and (3), and by groups (3) and (4), respectively. Strontium IRs and major and trace-element chemistry suggest that the tonalite-granodiorite and the later leucotonalite were obtained by partial melting of tholeiitic material with garnet as a controlling mineral phase. The leucogranite more likely resulted from remelting of tonalite-granodiorite under similar conditions with garnet again a controlling mineral phase. These four magmatic events constitute a massive addition to the crust from the mantle lasting about 250 Ma. About 90% of the silicic material was emplaced 80 Ma after activity began (tonalite-granodiorite stage) and at least 99% had been emplaced after 140 Ma (leucogranite stage)
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