Exploring patient and family satisfaction in pediatric neurological surgery

Introduction Patient and family satisfaction during outpatient visits is correlated with a continuance of care and likelihood to recommend the practice to others. Additionally, patient-family satisfaction can determine the success of the practice and influence medical outcomes. Utilizing a well-validated surveys instrument, patient and family satisfaction can be explored in the office setting. Methods During a consecutive 36 month period, a standardized and validated patient satisfaction survey instrument was provided to the family members of patients who presented to two pediatric neurosurgery clinics associated with Nemours Children\u27s Health System. The completed surveys were analyzed statistically to identify correlations between overall satisfaction, defined as “Likelihood to Recommend (LTR) the Practice”, and relevant practice and provider variables. Results The factors that exhibited the greatest correlation to LTR were: ‘Cheerfulness of Practice’ (r = 0.74), ‘Ability to Get Desired Appointment’ (r = 0.70), ‘Likelihood of Recommending Care Provider’ (r = 0.65), ‘Staff Worked Together’ (r = 0.65), and ‘Waiting Area Comfort and Pleasantness’ (r = 0.60). Discussion and conclusions Patient and family satisfaction surveys are useful for gaining insight into pediatric neurosurgical practices. Data from this cohort suggest that the environment in which patient care is delivered, timeliness of appointments and positive perceptions of the healthcare team correlate most strongly with overall satisfaction. © 201

A Fundamental Comparison of International Real Estate Returns

This study analyzes commercial real estate returns in Australia, Canada, the United Kingdom, and the United States over the period 1985-95, from the perspective of a U.S. investor. Because national indices can consist of differing property mixes, this study separately analyzes the office, retail, and warehouse sectors. Moreover, these analyses also convert total returns into their fundamental components: initial yield, growth in income, and shifts in capitalization rates. The paths of currency-adjusted income and asset values and, therefore, capitalization rates are also presented. Generally speaking, the fundamental components of retail returns across the four countries exhibit greater divergence than the office and warehouse sectors. It is interesting that the U.S. property sectors showed the worst performance, while the Australian retail and the British office and warehouse sectors were the best performers (both before and after currency adjustments). Additionally, the currency-adjusted Australian returns were adversely effected by exchange rate movements, while the British returns were positively effected. Lastly, the correlation of the quarterly percentage change in income was generally lower and less statistically significant that the correlation patterns observed among the other components of return. This might suggest that more idiosyncratic risk can be found in the real estate space markets (as proxied by income changes) than in the real estate capital markets (as proxied by the pricing of the income--that is, capitalization rates), which appear to be more globally influenced.

Gain Stabilization of a Submillimeter SIS Heterodyne Receiver

We have designed a system to stabilize the gain of a submillimeter heterodyne receiver against thermal fluctuations of the mixing element. In the most sensitive heterodyne receivers, the mixer is usually cooled to 4 K using a closed-cycle cryocooler, which can introduce ~1% fluctuations in the physical temperature of the receiver components. We compensate for the resulting mixer conversion gain fluctuations by monitoring the physical temperature of the mixer and adjusting the gain of the intermediate frequency (IF) amplifier that immediately follows the mixer. This IF power stabilization scheme, developed for use at the Submillimeter Array (SMA), a submillimeter interferometer telescope on Mauna Kea in Hawaii, routinely achieves a receiver gain stability of 1 part in 6,000 (rms to mean). This is an order of magnitude improvement over the typical uncorrected stability of 1 part in a few hundred. Our gain stabilization scheme is a useful addition to SIS heterodyne receivers that are cooled using closed-cycle cryocoolers in which the 4 K temperature fluctuations tend to be the leading cause of IF power fluctuations.Comment: 7 pages, 6 figures accepted to IEEE Transactions on Microwave Theory and Technique

The ages of quasar host galaxies

We present the results of fitting deep off-nuclear optical spectroscopy of radio-quiet quasars, radio-loud quasars and radio galaxies at z ~ 0.2 with evolutionary synthesis models of galaxy evolution. Our aim was to determine the age of the dynamically dominant stellar populations in the hos t galaxies of these three classes of powerful AGN. Some of our spectra display residual nuclear contamination at the shortest wavelengths, but the detailed quality of the fits longward of the 4000A break provide unequivocal proof, if further proof were needed, that quasars lie in massive galaxies with (at least at z ~ 0.2) evolved stellar populations. By fitting a two-component model we have separated the very blue (starburst and/or AGN contamination) from the redder underlying spectral energy distribution, and find that the hosts of all three classes of AGN are dominated by old stars of age 8 - 14 Gyr. If the blue component is attributed to young stars, we find that, at most, 1% of the baryonic mass of these galaxies is involved in star-formation activity at the epoch of observation. These results strongly support the conclusion reached by McLure et al. (1999) that the host galaxies of luminous quasars are massive ellipticals which formed prior to the peak epoch of quasar activity at z ~ 2.5.Comment: 24 pages, LaTeX, uses MNRAS style file, incorporates 19 postscript figures, final version, to be published in MNRA

A framework for time-dependent Ice Sheet Uncertainty Quantification, applied to three West Antarctic ice streams

Ice sheet models are the main tool to generate forecasts of ice sheet mass loss; a significant contributor to sea-level rise, thus knowing the likelihood of such projections is of critical societal importance. However, to capture the complete range of possible projections of mass loss, ice sheet models need efficient methods to quantify the forecast uncertainty. Uncertainties originate from the model structure, from the climate and ocean forcing used to run the model and from model calibration. Here we quantify the latter, applying an error propagation framework to a realistic setting in West Antarctica. As in many other ice-sheet modelling studies we use a control method to calibrate grid-scale flow parameters (parameters describing the basal drag and ice stiffness) with remotely-sensed observations. Yet our framework augments the control method with a Hessian-based Bayesian approach that estimates the posterior covariance of the inverted parameters. This enables us to quantify the impact of the calibration uncertainty on forecasts of sea-level rise contribution or volume above flotation (VAF), due to the choice of different regularisation strengths (prior strengths), sliding laws and velocity inputs. We find that by choosing different satellite ice velocity products our model leads to different estimates of VAF after 40 years. We use this difference in model output to quantify the variance that projections of VAF are expected to have after 40 years and identify prior strengths that can reproduce that variability. We demonstrate that if we use prior strengths suggested by L-curve analysis, as is typically done in ice-sheet calibration studies, our uncertainty quantification is not able to reproduce that same variability. The regularisation suggested by the L-curves is too strong and thus propagating the observational error through to VAF uncertainties under this choice of prior leads to errors that are smaller than those suggested by our 2-member &ldquo;sample&rdquo; of observed velocity fields. Additionally, our experiments suggest that large amounts of data points may be redundant, with implications for the error propagation of VAF.</p

Gender and racial/ethnic differences in the associations of urinary phthalate metabolites with markers of diabetes risk: national health and nutrition examination survey 2001–2008

Background: Phthalates are ubiquitous endocrine disrupting chemicals associated with diabetes. Although women and minorities are more likely to be exposed to phthalates, no prior studies have examined phthalate exposure and markers of diabetes risk evaluating effect modification by gender and race/ethnicity. Methods: We analyzed CDC data for 8 urinary phthalate metabolites from 3,083 non-diabetic, non-pregnant participants aged 12- < 80 years in the National Health and Nutrition Examination Survey (NHANES) 2001–2008. We used median regression to assess the associations between urinary phthalate metabolites and fasting blood glucose (FBG), fasting insulin and Homeostatic Model Assessment of insulin resistance (HOMA-IR), controlling for urinary creatinine as well as several sociodemographic and behavioral factors. Stratified analyses were conducted to compare the gender- and race/ethnicity-specific patterns for the associations. Results: Urinary levels of several phthalate metabolites, including MBzP, MnBP, MiBP, MCPP and ∑DEHP showed significant positive associations with FBG, fasting insulin and HOMA-IR. No clear difference was noted between men and women. Mexican-Americans and non-Hispanic blacks had stronger dose–response relationships for MnBP, MiBP, MCPP and ∑DEHP compared to non-Hispanic whites. For example, the highest quartile of MiBP relative to its lowest quartile showed a median FBG increase of 5.82 mg/dL (95% CI: 3.77, 7.87) in Mexican-Americans, 3.63 mg/dL (95% CI: 1.23, 6.03) in blacks and 1.79 mg/dL (95% CI: -0.29, 3.87) in whites. Conclusions: The findings suggest that certain populations may be more vulnerable to phthalates with respect to disturbances in glucose homeostasis. Whether endocrine disrupting chemicals contribute to gender and racial/ethnic differences in diabetes risk will be an important area for further study

Phenex: Ontological Annotation of Phenotypic Diversity

Phenex is a platform-independent desktop application designed to facilitate efficient and consistent annotation of phenotypic variation using Entity-Quality syntax, drawing on terms from community ontologies for anatomical entities, phenotypic qualities, and taxonomic names. Despite the centrality of the phenotype to so much of biology, traditions for communicating information about phenotypes are idiosyncratic to different disciplines. Phenotypes seem to elude standardized descriptions due to the variety of traits that compose them and the difficulty of capturing the complex forms and subtle differences among organisms that we can readily observe. Consequently, phenotypes are refractory to attempts at data integration that would allow computational analyses across studies and study systems. Phenex addresses this problem by allowing scientists to employ standard ontologies and syntax to link computable phenotype annotations to evolutionary character matrices, as well as to link taxa and specimens to ontological identifiers. Ontologies have become a foundational technology for establishing shared semantics, and, more generally, for capturing and computing with biological knowledge

fenics_ice 1.0: A framework for quantifying initialisation uncertainty for time-dependent ice-sheet models

Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise is and hence crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice sheet models.</p