Department of Internal Medicine, Center for Experimental and Molecular Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Room: T1-0-240, Amsterdam, The Netherlands.
Department of Radiology and Nuclear Medicine, Amsterdam UMC, University of Amsterdam, Meibergdreef 9, 1105 AZ, Amsterdam, The Netherlands.
Eur Radiol Exp. 2020 Jul 17;4(1):44. doi: 10.1186/s41747-020-00169-y.
A challenge in imaging research is a 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.
OPTIMACT is a randomised 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 the validity, 60 randomly selected cases not referred to the panel by the students and the residents were reassessed by the specialists.
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%).
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).
影像学研究中的一个挑战是对研究参与者进行诊断分类。我们假设，结构化方法将是有效的，并且只要需要，由医学生、住院医师和专家小组进行分类，与由专家对所有患者进行分类一样有效。
OPTIMACT 是一项旨在评估在急诊科用超低剂量胸部 CT 替代胸部 X 光的有效性的随机试验。我们制定了一本手册，其中包含诊断指南，并从 OPTIMACT 中纳入的 2418 名参与者中随机选择了 240 例。每个病例都由两名医学生独立分类，如果他们意见不一致，则由学生和住院医师在共识会议上进行分类。没有达成共识的病例和分类为复杂的病例由医学专家小组进行评估。为了评估有效性，由学生和住院医师未提交给专家小组的 60 例随机选择的病例由专家重新评估。
总体而言，学生和必要时的住院医师能够对 240 例中的 183 例（76%的一致性；95%置信区间 [CI] 71-82%）分配诊断。我们观察到学生和住院医师与医学专家在 50/60 例（83%的一致性；95%CI 74-93%）之间的一致性。
一种结构化的方法，即评估员根据医疗经验水平对研究参与者分配诊断标签，是一种高效且有效的分类方法，限制了医学专家的工作量。我们提出了一种可行的选择，用于对大型影像学试验中的研究参与者进行分类（荷兰国家试验登记号 NTR6163）。
