Decision-making about corticosteroids in relapses of multiple sclerosis - development of a questionnaire based on the theory of planned behaviour.

BACKGROUND: Relapses of multiple sclerosis are burdensome events and entail potentially lasting loss of function. People with multiple sclerosis have to consider corticosteroids, providing limited benefits and the risk of adverse effects. OBJECTIVE: To develop and validate a questionnaire investigating the internal process of people with multiple sclerosis making decisions about corticosteroids. METHODS: The questionnaire is structured by three domains, attitude, subjective social norm, and perceived behavioural control, which according to the theory of planned behaviour determine action planning. The development is inspired by a previous questionnaire studying decisions on immunotherapy. The questionnaire was tested in qualitative think-aloud interviews (n=10) for feasibility and comprehensibility and in an online survey (n=203) to assess construct and criterion validity. RESULTS: The 18-item questionnaire was considered feasible and comprehensible. It predicted the intention to receive corticosteroids in up to 82.3% of cases. "Subjective social norm" impacted most on intention. The questionnaire also proved sensitive for autonomy preferences of people with multiple sclerosis. CONCLUSION: This study shows that the questionnaire appropriately explains the internal process people with multiple sclerosis run through when considering corticosteroids. It can be used to inform developments of tailored support for people with multiple sclerosis in making informed decisions about relapse management.

An adaptive, object oriented strategy for base calling in DNA sequence analysis.

An algorithm has been developed for the determination of nucleotide sequence from data produced in fluorescence-based automated DNA sequencing instruments employing the four-color strategy. This algorithm takes advantage of object oriented programming techniques for modularity and extensibility. The algorithm is adaptive in that data sets from a wide variety of instruments and sequencing conditions can be used with good results. Confidence values are provided on the base calls as an estimate of accuracy. The algorithm iteratively employs confidence determinations from several different modules, each of which examines a different feature of the data for accurate peak identification. Modules within this system can be added or removed for increased performance or for application to a different task. In comparisons with commercial software, the algorithm performed well.