Preliminary Experience in Treatment of Papillary and Macular Retinoblastoma: Evaluation of Local Control and Local Complications After Treatment with Linear Accelerator-Based Stereotactic Radiotherapy with Micromultileaf Collimator as Second-Line or Salvage Treatment after chemotherapy.

PURPOSE: To determine the local control and complication rates for children with papillary and/or macular retinoblastoma progressing after chemotherapy and undergoing stereotactic radiotherapy (SRT) with a micromultileaf collimator. METHODS AND MATERIALS: Between 2004 and 2008, 11 children (15 eyes) with macular and/or papillary retinoblastoma were treated with SRT. The mean age was 19 months (range, 2-111). Of the 15 eyes, 7, 6, and 2 were classified as International Classification of Intraocular Retinoblastoma Group B, C, and E, respectively. The delivered dose of SRT was 50.4 Gy in 28 fractions using a dedicated micromultileaf collimator linear accelerator. RESULTS: The median follow-up was 20 months (range, 13-39). Local control was achieved in 13 eyes (87%). The actuarial 1- and 2-year local control rates were both 82%. SRT was well tolerated. Late adverse events were reported in 4 patients. Of the 4 patients, 2 had developed focal microangiopathy 20 months after SRT; 1 had developed a transient recurrence of retinal detachment; and 1 had developed bilateral cataracts. No optic neuropathy was observed. CONCLUSIONS: Linear accelerator-based SRT for papillary and/or macular retinoblastoma in children resulted in excellent tumor control rates with acceptable toxicity. Additional research regarding SRT and its intrinsic organ-at-risk sparing capability is justified in the framework of prospective trials.

Multiple-scale hydrological and geophysical data integration through non-linear Bayesian sequential simulation

Significant progress has been made with regard to the quantitative integration of geophysical and hydrological data at the local scale. However, extending the corresponding approaches to the scale of a field site represents a major, and as-of-yet largely unresolved, challenge. To address this problem, we have developed downscaling procedure based on a non-linear Bayesian sequential simulation approach. The main objective of this algorithm is to estimate the value of the sparsely sampled hydraulic conductivity at non-sampled locations based on its relation to the electrical conductivity logged at collocated wells and surface resistivity measurements, which are available throughout the studied site. The in situ relationship between the hydraulic and electrical conductivities is described through a non-parametric multivariatekernel density function. Then a stochastic integration of low-resolution, large-scale electrical resistivity tomography (ERT) data in combination with high-resolution, local-scale downhole measurements of the hydraulic and electrical conductivities is applied. The overall viability of this downscaling approach is tested and validated by comparing flow and transport simulation through the original and the upscaled hydraulic conductivity fields. Our results indicate that the proposed procedure allows obtaining remarkably faithful estimates of the regional-scale hydraulic conductivity structure and correspondingly reliable predictions of the transport characteristics over relatively long distances.

Local Distance-Based Generalized Linear Models using the dbstats package for R

This paper introduces local distance-based generalized linear models. These models extend (weighted) distance-based linear models firstly with the generalized linear model concept, then by localizing. Distances between individuals are the only predictor information needed to fit these models. Therefore they are applicable to mixed (qualitative and quantitative) explanatory variables or when the regressor is of functional type. Models can be fitted and analysed with the R package dbstats, which implements several distancebased prediction methods.

Review of governance and accountability mechanisms in the General Medical Services schemes

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Future Dementia Care in Ireland: Sharing the Evidence to Mobilise Action

A major challenge facing societies across the world today is population ageing. The fact that people are living longer and the notable improvements in the overall quality of life and well-being of older people today needs to be celebrated. However, population ageing will inevitably result in an increase in disability and a very significant increase in the incidence of age-related health problems especially Alzheimer's disease and the related dementias. Click here to download PDF 2.5mb

Binary classification of dyslipidemia from the waist-to-hip ratio and body mass index: a comparison of linear, logistic, and CART models.

BACKGROUND: We sought to improve upon previously published statistical modeling strategies for binary classification of dyslipidemia for general population screening purposes based on the waist-to-hip circumference ratio and body mass index anthropometric measurements. METHODS: Study subjects were participants in WHO-MONICA population-based surveys conducted in two Swiss regions. Outcome variables were based on the total serum cholesterol to high density lipoprotein cholesterol ratio. The other potential predictor variables were gender, age, current cigarette smoking, and hypertension. The models investigated were: (i) linear regression; (ii) logistic classification; (iii) regression trees; (iv) classification trees (iii and iv are collectively known as "CART"). Binary classification performance of the region-specific models was externally validated by classifying the subjects from the other region. RESULTS: Waist-to-hip circumference ratio and body mass index remained modest predictors of dyslipidemia. Correct classification rates for all models were 60-80%, with marked gender differences. Gender-specific models provided only small gains in classification. The external validations provided assurance about the stability of the models. CONCLUSIONS: There were no striking differences between either the algebraic (i, ii) vs. non-algebraic (iii, iv), or the regression (i, iii) vs. classification (ii, iv) modeling approaches. Anticipated advantages of the CART vs. simple additive linear and logistic models were less than expected in this particular application with a relatively small set of predictor variables. CART models may be more useful when considering main effects and interactions between larger sets of predictor variables.

Regional guidelines for nurses when sharing information with NIGALA

Effective safeguarding children practice depends upon information sharing, collaboration and understanding between families, agencies and professionals. Nurses are required to work in partnership with other disciplines and agencies to safeguard and promote the health and wellbeing of children and young people. This includes working in partnership with the guardian ad litem (guardian) appointed by the court when making decisions regarding the best interests of a child or young person.This guidance replaces previous regional guidance. Regional implementation will ensure that information held by nurses is shared with the guardian in a consistent, timely and appropriate manner, so that informed decisions can be made in the best interests of children and young people.