Predicting success in graduate entry medical students undertaking a graduate entry medical program (GEM)

Background: Success in undergraduate medical courses in the UK can be predicted by school exit examination (A level) grades. There are no documented predictors of success in UK graduate entry medicine (GEM) courses. This study looks at the examination performance of GEM students to identify factors which may predict success; of particular interest was A level score. Methods: Data was collected for students graduating in 2004, 2005 and 2006, including demographic details (age and gender), details of previous academic achievement (A level total score and prior degree) and examination results at several points during the degree course. Results: Study group comprised 285 students. Statistical analyses identified no significant variables when looking at clinical examinations. Analysis of pass/fail data for written examinations showed no relationship with A level score. However, both percentage data for the final written examination and the analysis of the award of honours showed A level scores of AAB or higher were associated with better performance (p < 0.001). Discussion: A prime objective of introducing GEM programs was to diversify admissions to medical school. In trying to achieve this, medical schools have changed selection criteria. The findings in this study justify this by proving that A level score was not associated with success in either clinical examinations or passing written examinations. Despite this, very high achievements at A level do predict high achievement during medical school. Conclusions: This study shows that selecting graduate medical students with the basic requirement of an upper-second class honours degree is justifiable and does not disadvantage students who may not have achieved high scores in school leaver examinations

Representing temporal dependencies in human activity recognition.

Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared over time. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs and consider the temporal dependencies that exist in binary ambient sensor data in order to produce case-based representations. These LSTM approaches are compared to the performance of a selection of baseline classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Representing temporal dependencies in smart home activity recognition for health monitoring.

Long term health conditions, such as fall risk, are traditionally diagnosed through testing performed in hospital environments. Smart Homes offer the opportunity to perform continuous, long-term behavioural and vitals monitoring of residents, which may be employed to aid diagnosis and management of chronic conditions without placing additional strain on health services. A profile of the resident’s behaviour can be produced from sensor data, and then compared overtime. Activity Recognition is a primary challenge for profile generation, however many of the approaches adopted fail to take full advantage of the inherent temporal dependencies that exist in the activities taking place. Long Short Term Memory (LSTM) is a form of recurrent neural network that uses previously learned examples to inform classification decisions. In this paper we present a variety of approaches to human activity recognition using LSTMs which consider the temporal dependencies present in the sensor data in order to produce richer representations and improved classification accuracy. The LSTM approaches are compared to the performance of a selection of base line classification algorithms on several real world datasets. In general, it was found that accuracy in LSTMs improved as additional temporal information was presented to the classifier

Monitoring health in smart homes using simple sensors.

We consider use of an ambient sensor network, installed in Smart Homes, to identify low level events taking place which can then be analysed to generate a resident's profile of activities of daily living (ADLs). These ADL profiles are compared to both the resident's typical profile and to known 'risky' profiles to support evidence-based interventions. Human activity recognition to identify ADLs from sensor data is a key challenge, a windowbased representation is compared on four existing datasets. We find that windowing works well, giving consistent performance. We also introduce FITsense, which is building a Smart Home environment to specifically identify increased risk of falls to allow interventions before falls occurs

Vitamin D and acute and severe illness – a mechanistic and pharmacokinetic perspective

The COVID-19 pandemic has generated high interest in factors modulating risk of infection, disease severity and recovery. Vitamin D has received interest since it is known to modulate immune function and vitamin D deficiency is associated with increased risk of respiratory infections and adverse health outcomes in severely ill patients. There are no population representative data on the direct relationship between vitamin D status and SARS-CoV-2 infection risk and severity of COVID-19. Data from intervention studies are limited to 4 studies. Here we summarise findings regarding vitamin D status and metabolism and their alterations during severe illness, relevant to COVID-19 patients. Further, we summarise vitamin D intervention studies with respiratory disease outcomes and in critically ill patients and provide an overview of relevant patient and population guidelines. Vitamin D deficiency is highly prevalent in hospitalised patients, particularly when critically ill including those with COVID-19. Acute and critical illness leads to pronounced changes in vitamin D metabolism and status, suggestive of increased requirements. This needs to be considered in the interpretation of potential links between vitamin D status and disease risk and severity and for patient management. There is some evidence that vitamin D supplementation decreases the risk of respiratory tract infections, while supplementation of ICU patients has shown little effect on disease severity or length of treatment. Considering the high prevalence of deficiency and low risks associated with supplementation, pro-actively applying current population and patient management guidelines to prevent, monitor and correct vitamin D deficiency is appropriate