A study’s got to know its limitations

Background: All research has room for improvement, but authors do not always clearly acknowledge the limitations of their work. In this brief report, we sought to identify the prevalence of limitations statements in the medRxiv COVID-19 SARS-CoV-2 dataset. Methods: We combined automated methods with manual review to analyse manuscripts for the presence, or absence, either of a defined limitations section in the text, or as part of the general discussion. Results: We identified a structured limitations statement in 28% of the manuscripts, and overall 52% contained at least one mention of a study limitation. Over one-third of manuscripts contained none of the terms that might typically be associated with reporting of limitations. Overall our method performed with precision of 0.97 and recall of 0.91. Conclusion: The presence or absence of limitations statements can be identified with reasonable confidence using automated tools. We suggest that it might be beneficial to require a defined, structured statement about study limitations, either as part of the submission process, or clearly delineated within the manuscript

Lexical patterns, features and knowledge resources for coreference resolution in clinical notes

Generation of entity coreference chains provides a means to extract linked narrative events from clinical notes, but despite being a well-researched topic in natural language processing, general- purpose coreference tools perform poorly on clinical texts. This paper presents a knowledge-centric and pattern-based approach to resolving coreference across a wide variety of clinical records comprising discharge summaries, progress notes, pathology, radiology and surgical reports from two corpora (Ontology Development and Information Extraction (ODIE) and i2b2/VA). In addition, a method for generating coreference chains using progressively pruned linked lists is demonstrated that reduces the search space and facilitates evaluation by a number of metrics. Independent evaluation results show an F-measure for each corpus of 79.2% and 87.5%, respectively, which offers performance at least as good as human annotators, greatly increased performance over general- purpose tools, and improvement on previously reported clinical coreference systems. The system uses a number of open-source components that are available to download