Automatic categorization of self-acknowledged limitations in randomized controlled trial publications.

ABSTRACT:

Objective

Acknowledging study limitations in a scientific publication is a crucial element in scientific transparency and progress. However, limitation reporting is often inadequate. Natural language processing (NLP) methods could support automated reporting checks, improving research transparency. In this study, our objective was to develop a dataset and NLP methods to detect and categorize self-acknowledged limitations (e.g., sample size, blinding) reported in randomized controlled trial (RCT) publications.

Methods

We created a data model of limitation types in RCT studies and annotated a corpus of 200 full-text RCT publications using this data model. We fine-tuned BERT-based sentence classification models to recognize the limitation sentences and their types. To address t

SUBMITTER: Lan M 

PROVIDER: S-EPMC11807350 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature