Grades do have a bearing

I am afraid that I need to challenge the assertion made by Rachel Airley in her letter (PJ, 10 March 2012, p308) that “there is no clear cut evidence that UCAS points obtained at school have any bearing on final degree performance”. Research from the Higher Education Funding Council for England — the body responsible for the distribution of funding to universities in England — shows that educational attainment before entry to higher education (ie, A-level grades) is the most important factor in determining academic success on undergraduate degree programmes.1,2 Indeed, research I have recently conducted on a cohort of MPharm students at Aston University (which will hopefully be published in a peer-reviewed academic journal shortly) demonstrates a strong positive correlation between UCAS Tariff points per A-level and final degree classification. As Dr Airley highlights in her letter, competition for places on MPharm programmes remains fierce and, in response to high levels of demand, her own institution has increased its standard entry offer. If UCAS Tariff points have little predictive ability of performance on the MPharm programme then, aside from minimising the administrative burden that the admissions process places on an institution, what is the logic behind increasing standard entry offers?

Modulating membership grades to gain consensus for fuzzy set uncertainty values in a clinical decision support system

This paper deals with a very important issue in any knowledge engineering discipline: the accurate representation and modelling of real life data and its processing by human experts. The work is applied to the GRiST Mental Health Risk Screening Tool for assessing risks associated with mental-health problems. The complexity of risk data and the wide variations in clinicians' expert opinions make it difficult to elicit representations of uncertainty that are an accurate and meaningful consensus. It requires integrating each expert's estimation of a continuous distribution of uncertainty across a range of values. This paper describes an algorithm that generates a consensual distribution at the same time as measuring the consistency of inputs. Hence it provides a measure of the confidence in the particular data item's risk contribution at the input stage and can help give an indication of the quality of subsequent risk predictions. © 2010 IEEE.