TY  - JOUR
T1  - First Steps Towards a Risk of Bias Corpus of Randomized Controlled Trials
A1  - Dhrangadhariya, Anjani
A1  - Hilfiker, Roger
A1  - Sattelmayer, Martin
A1  - Giacomino, Katia
A1  - Caliesch, Rahel
A1  - Simone, Elsig
A1  - Naderi, Nona
A1  - Müller, Henning
JA  - CARING IS SHARING--EXPLOITING THE VALUE IN DATA FOR HEALTH AND INNOVATION
T3  - Studies in Health Technology and Informatics
Y1  - 2023
SP  - 586
UR  - https://ebooks.iospress.nl/doi/10.3233/SHTI230210
M2  - doi: 10.3233/SHTI230210
KW  - corpus
KW  - Information extraction
KW  - Natural Language Processing
KW  - risk of bias
KW  - systematic reviews
N2  - Risk of bias (RoB) assessment of randomized clinical trials (RCTs) is vital to conducting systematic reviews. Manual RoB assessment for hundreds of RCTs is a cognitively demanding, lengthy process and is prone to subjective judgment. Supervised machine learning (ML) can help to accelerate this process but requires a hand-labelled corpus. There are currently no RoB annotation guidelines for randomized clinical trials or annotated corpora. In this pilot project, we test the practicality of directly using the revised Cochrane RoB 2.0 guidelines for developing an RoB annotated corpus using a novel multi-level annotation scheme. We report inter-annotator agreement among four annotators who used Cochrane RoB 2.0 guidelines. The agreement ranges between 0% for some bias classes and 76% for others. Finally, we discuss the shortcomings of this direct translation of annotation guidelines and scheme and suggest approaches to improve them to obtain an RoB annotated corpus suitable for ML.
ER  -