Accrual and drop out in a primary prevention randomised controlled trial: qualitative study

<p>Abstract</p> <p>Background</p> <p>Recruitment and retention of participants are critical to the success of a randomised controlled trial. Gaining the views of potential trial participants who decline to enter a trial and of trial participants who stop the trial treatment is important and can help to improve study processes. Limited research on these issues has been conducted on healthy individuals recruited for prevention trials in the community.</p> <p>Methods</p> <p>Semi-structured interviews with people who were eligible but had declined to participate in the Aspirin for Asymptomatic Atherosclerosis (AAA) trial (N = 11), and AAA trial participants who had stopped taking the trial medication (N = 11). A focus group with further participants who had stopped taking the trial medication (N = 6). (Total participants N = 28).</p> <p>Results</p> <p>Explanations for declining to participate could be divided into two groups: the first group were characterised by a lack of necessity to participate and a tendency to prioritise other largely mundane problems. The second group's concern was with a high level of perceived risk from participating.</p> <p>Explanations for stopping trial medication fell into four categories: side effects attributed to the trial medication; starting on aspirin or medication contraindicating to aspirin; experiencing an outcome event, and changing one's mind.</p> <p>Conclusions</p> <p>These results indicate that when planning trials (especially in preventive medicine) particular attention should be given to designing appropriate recruitment materials and processes that fully inform potential recruits of the risks and benefits of participation.</p> <p>Trial registration</p> <p>ISRCTN66587262</p

Neural networks for robotic detection of mastitis in dairy cows: Netherlands and New Zealand perspectives

This paper describes two parts of a continuing research project on developing neural network models for automated early diagnosis of mastitis in dairy cows milked by robotic milking systems. The justification for the project is that mastitis costs industry millions of dollars and severely compromises the health of cows. In the first part, robotic milking data from the Netherlands were used to develop Self Organising Map (SOM) networks providing 96% accuracy and revealing the nature of healthy and sick data regions. In the second part, New Zealand robotic data were used to map the development of mastitis from healthy, marginally ill through to ill stages. Models revealed that the characteristics of mastitis and healthy cases in terms of mastitis indicators are similar for the two countries