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Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach. / For the OPTIMACT Study Group.

In: European Radiology Experimental, Vol. 4, No. 1, 44, 01.12.2020.

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For the OPTIMACT Study Group. / Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach. In: European Radiology Experimental. 2020 ; Vol. 4, No. 1.

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@article{c24d74e192ab4b5b9f21a615ca441f59,
title = "Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach",
abstract = "Background: 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. Methods: 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. 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).",
keywords = "Emergency service (hospital), Methods, Observer variation, Radiography (thoracic), Tomography x-ray, computed",
author = "{van Engelen}, {Tjitske S. R.} and Kanglie, {Maadrika M. N. P.} and {van den Berk}, {Inge A. H.} and Bouwman, {Merel L. J.} and Suhooli, {Hind J. M.} and Heckert, {Sascha L.} and Jaap Stoker and Bossuyt, {Patrick M. M.} and Prins, {Jan M.} and {For the OPTIMACT Study Group} and Jouke Annema and Beenen, {Ludo F. M.} and Shandra Bipat and Paul Bresser and Marcel Dijkgraaf and Jos Donker and van Engelen, {Tjitske S. R.} and Betty Frankem{\"o}lle and Maarten Groenink and Hochheimer, {Suzanne M. R.} and Frits Holleman and Dorine Hulzebosch and Mitran Keijzers and {van der Lee}, Ivo and Peter Leenhouts and Jan Luitse and Meijboom, {Lilian J.} and Saskia Middeldorp and {Montauban van Swijndregt}, Alexander and {de Mony{\'e}}, Wouter and Jacqueline Otker and Milan Ridderikhof and Romijn, {Johannes A.} and Schoonderwoerd, {Antoinet J. N.} and Sprengers, {Ralf W.} and Taal, {Elizabeth M.} and Michiel Winter",
year = "2020",
month = dec,
day = "1",
doi = "10.1186/s41747-020-00169-y",
language = "English",
volume = "4",
journal = "European Radiology Experimental",
issn = "2509-9280",
publisher = "Springer Open",
number = "1",

}

RIS

TY - JOUR

T1 - Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach

AU - van Engelen, Tjitske S. R.

AU - Kanglie, Maadrika M. N. P.

AU - van den Berk, Inge A. H.

AU - Bouwman, Merel L. J.

AU - Suhooli, Hind J. M.

AU - Heckert, Sascha L.

AU - Stoker, Jaap

AU - Bossuyt, Patrick M. M.

AU - Prins, Jan M.

AU - For the OPTIMACT Study Group

AU - Annema, Jouke

AU - Beenen, Ludo F. M.

AU - Bipat, Shandra

AU - Bresser, Paul

AU - Dijkgraaf, Marcel

AU - Donker, Jos

AU - van Engelen, Tjitske S. R.

AU - Frankemölle, Betty

AU - Groenink, Maarten

AU - Hochheimer, Suzanne M. R.

AU - Holleman, Frits

AU - Hulzebosch, Dorine

AU - Keijzers, Mitran

AU - van der Lee, Ivo

AU - Leenhouts, Peter

AU - Luitse, Jan

AU - Meijboom, Lilian J.

AU - Middeldorp, Saskia

AU - Montauban van Swijndregt, Alexander

AU - de Monyé, Wouter

AU - Otker, Jacqueline

AU - Ridderikhof, Milan

AU - Romijn, Johannes A.

AU - Schoonderwoerd, Antoinet J. N.

AU - Sprengers, Ralf W.

AU - Taal, Elizabeth M.

AU - Winter, Michiel

PY - 2020/12/1

Y1 - 2020/12/1

N2 - Background: 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. Methods: 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. 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).

AB - Background: 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. Methods: 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. 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).

KW - Emergency service (hospital)

KW - Methods

KW - Observer variation

KW - Radiography (thoracic)

KW - Tomography x-ray

KW - computed

UR - http://www.scopus.com/inward/record.url?scp=85088120568&partnerID=8YFLogxK

U2 - 10.1186/s41747-020-00169-y

DO - 10.1186/s41747-020-00169-y

M3 - Article

C2 - 32676897

VL - 4

JO - European Radiology Experimental

JF - European Radiology Experimental

SN - 2509-9280

IS - 1

M1 - 44

ER -

ID: 12528250