168 Chapter 8 Abstract Background: A challenge in imaging research is diagnostic classification of study participants. We hypothesised that a structured approach would be efficient and that classification by medical students, residents, and an expert panel whenever necessary, would be as valid as classification of all patients by experts. Methods: OPTIMACT is a randomized trial designed to evaluate the effectiveness of replacing chest x-ray for ultra-low-dose chest computed tomography (CT) at the emergency department. We developed a handbook with diagnostic guidelines and randomly selected 240 cases from 2,418 participants enrolled in OPTIMACT. Each case was independently classified by two medical students and, if they disagreed, by the students and a resident in a consensus meeting. Cases without consensus and cases classified as complex were assessed by a panel of medical specialists. To evaluate validity, 60 randomly selected cases not referred to the panel by the students and the residents were reassessed by the specialists. Results: Overall, the students and, if necessary, residents were able to assign a diagnosis in 183 of the 240 cases (76% concordance; 95% confidence interval (CI) 71–82%). We observed agreement between students and residents versus medical specialists in 50/60 cases (83% concordance; 95% CI 74–93%). Conclusions: A structured approach in which study participants are assigned diagnostic labels by assessors with increasing levels of medical experience was an efficient and valid classification method, limiting the workload for medical specialists. We presented a viable option for classifying study participants in large-scale imaging trials (Netherlands National Trial Register number NTR6163). Key points • Valid diagnostic classification of study participants is a prerequisite for imaging research. • A structured approach, using the expertise of students, residents, and medical specialists, was an efficient classification method of patients, limiting the workload for medical specialists. • Medical students and residents could classify the diagnosis of patients suspected of pulmonary disease at the emergency department with high validity, as compared to a panel of medical specialists.
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