A direct consequence of these challenges is the work involved in manually screening a large number of records on an annual basis. Finally, the search is not limited by condition or disease, setting, study type or intervention. ![]() Furthermore, no single database specializes in this type of methodological research and it is likely to be found across a wide range of literature. core domain set, core measurement set, minimum outcome set), and they do not appear to be categorized consistently across different databases. The term ‘core outcome set’ has not been commonly used until recently, and is still not consistently applied with many variations employed to describe this type of study (e.g. We encounter challenges in undertaking this comprehensive approach, such as the variability in free text terms and index terms used for COS development, further confounded by the absence of a specific index term or Medical Subject Heading (MeSH) main heading for this study type ( 9). The inclusion and exclusion criteria are described in more detail in the original systematic review ( 8). Relevant studies therefore describe the development of a COS, regardless of any restrictions by age, health condition or setting. Studies are eligible for inclusion if they have applied methodology for determining which outcomes or outcome domains should be measured in clinical trials or other forms of health research. Full texts of potentially relevant articles are obtained to assess for inclusion (stage 2). Titles and abstracts are read to assess eligibility of studies for inclusion in the review (stage 1). Relevant studies are added to the database as they are found, but the annual update to the systematic review is necessary to ensure completeness.Ī two-stage process is employed to screen records and identify relevant studies. The database is an integral resource not only to the development of COS, but also to the uptake of COS in research and in the avoidance of unnecessary duplication and waste of scarce resources ( 7).Ī survey demonstrated that the database is also used by a variety of other users in addition to COS developers, including clinical trialists, systematic reviewers, auditors, guideline developers and funders ( 11). The database was originally populated through completion of a systematic review ( 8), which is annually updated to include all published COS, currently up to and including December 2017 ( 5, 7, 10, 11). Since 2011, COMET has maintained a public repository of studies relevant to the development of COS (The COMET database, ). COMET facilitates the development and application of COS, by bringing relevant material together and thus making it more accessible. A COS is an agreed standardized set of outcomes that should be measured and reported, as a minimum, in all trials for a specific clinical area ( 28). These problems are being addressed through the development and use of core outcome sets (COS). One such database is maintained by the Core Outcome Measures in Effectiveness Trials (COMET) Initiative, which aims to improve the usefulness of outcomes in research and help tackle problems such as outcome reporting bias, inconsistency and lack of importance or relevance of outcomes to patients. Curated databases play a major role in helping researchers and clinicians access this data, by selecting articles and specific facts of interest in the subfield of biomedicine they address ( 12, 17). ![]() We judged this to be an acceptable trade-off for this systematic review, and the method is now being used for the next round of the Comet database update.Ī wealth of biomedical information is buried in the free text of scientific publications. We estimated that using automatic screening would yield a workload reduction of at least 75% while keeping the number of missed references around 2%. Data from the original systematic review and its four first review updates were used to train the model and evaluate performance. In this study we have evaluated a machine learning approach based on logistic regression to automatically rank the candidate articles. In order to reduce the workload and facilitate more timely updates we are evaluating machine learning methods to reduce the number of references needed to screen. ![]() One such database is the freely accessible Comet Core Outcome Set database, which was originally populated using manual screening in an annually updated systematic review. Curated databases of scientific literature play an important role in helping researchers find relevant literature, but populating such databases is a labour intensive and time-consuming process.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |