Reducing the learning curve for the treatment of morphoeic (sclerosing) basal cell carcinoma of the face.

The treatment of morphoeic (or sclerosing) basal cell carcinoma (mBCC) of the face is associated with high rates of incomplete excision and recurrence. A principal risk factor for incomplete resection is the grade of surgeon. We did a prospective, randomised study of 40 consecutive patients with mBCC of the face. The extent of the tumour was assessed under standard conditions by consultant surgeons and compared with assessments by resident surgeons with the help of the Varioscope, a combination of microscope and loupe glasses with strong illumination and a maximal magnification of 7x. The data from a former retrospective study of all excisions of mBCC of the face during a five-year period at the hospital served as control. Residents with the support of the Varioscope achieved a rate of incomplete excisions similar to that of consultants under standard conditions. There was a significant reduction of the rate of incomplete resections by resident surgeons thanks to high magnification and good lighting (p=0.02). High magnification and good lighting were useful in learning how to recognise skin changes associated with mBCC of the face and achieving a low rate of incomplete excisions.

The Northern Ireland Action Plan for Learning Disability Nursing

The Northern Ireland Action Plan for Learning Disability Nursing

Learning Disability Service Framework (PDF 3MB)

Learning Disability Service Framework - Easy Access Version

Learning Disability Service Framework

Easy Access Version

RQIA Review of the implementation of Promoting Quality Care (PQC) Good Practice Guidance on the Assessment and Management of Risk in Mental Health and Learning Disability Services (PDF1.07MB)

Overview Report October 2012

A Workforce Learning Strategy for the Northern Ireland Health and Social Care Services 2009-2014 (PDF 318KB)

A Workforce Learning Strategy for the Northern Ireland Health and Social Care Services 2009-2014

Organizational form and business performance

This project deals with the generation of profitability and the distribution of its benefits. Inspired by Davis (1947, 1955), we define profitability as the ratio of revenue to cost. Profitability is not as popular a measure of business financial performance as profit, the difference between revenue and cost. Regardless of its popularity, however, profitability is surely a useful financial performance measure. Our primary objective in this project is to identify the factors that generate change in profitability. One set of factors, which we refer to as sources, consists of changes in quantities and prices of outputs and inputs. Individual quantity changes aggregate to the overall impact of quantity change on profitability change, which we call productivity change. Individual price changes aggregate to the overall impact of price change on profitability change, which we call price recovery change. In this framework profitability change consists exclusively of productivity change and price recovery change. A second set of factors, which we refer to as drivers, consists of phenomena such as technical change, change in the efficiency of resource allocation, and the impact of economies of scale. The ability of management to harness these factors drives productivity change, which is one component of profitability change. Thus the term sources refers to quantities and prices of individual outputs and inputs, whose changes influence productivity change or price recovery change, either of which influences profitability change. The term drivers refers to phenomena related to technology and management that influence productivity change (but not price recovery change), and hence profitability change.

Extreme Precipitation Modelling Using Geostatistics and Machine Learning Algorithms

The paper presents an approach for mapping of precipitation data. The main goal is to perform spatial predictions and simulations of precipitation fields using geostatistical methods (ordinary kriging, kriging with external drift) as well as machine learning algorithms (neural networks). More practically, the objective is to reproduce simultaneously both the spatial patterns and the extreme values. This objective is best reached by models integrating geostatistics and machine learning algorithms. To demonstrate how such models work, two case studies have been considered: first, a 2-day accumulation of heavy precipitation and second, a 6-day accumulation of extreme orographic precipitation. The first example is used to compare the performance of two optimization algorithms (conjugate gradients and Levenberg-Marquardt) of a neural network for the reproduction of extreme values. Hybrid models, which combine geostatistical and machine learning algorithms, are also treated in this context. The second dataset is used to analyze the contribution of radar Doppler imagery when used as external drift or as input in the models (kriging with external drift and neural networks). Model assessment is carried out by comparing independent validation errors as well as analyzing data patterns.