Combining meta-epidemiological study datasets on commercial funding of randomised clinical trials: Database, methods, and descriptive results of the COMFIT study.

Randomised trials are often funded by commercial companies and methodological studies support a widely held suspicion that commercial funding may influence trial results and conclusions. However, these studies often have a risk of confounding and reporting bias. The risk of confounding is markedly reduced in meta-epidemiological studies that compare fairly similar trials within meta-analyses, and risk of reporting bias is reduced with access to unpublished data.

Data extraction methods for systematic review (semi)automation: Update of a living systematic review.

The reliable and usable (semi)automation of data extraction can support the field of systematic review by reducing the workload required to gather information about the conduct and results of the included studies. This living systematic review examines published approaches for data extraction from reports of clinical studies. We systematically and continually search PubMed, ACL Anthology, arXiv, OpenAlex via EPPI-Reviewer, and the  .

Data extraction methods for systematic review (semi)automation: A living review protocol.

Researchers in evidence-based medicine cannot keep up with the amounts of both old and newly published primary research articles. Support for the early stages of the systematic review process - searching and screening studies for eligibility - is necessary because it is currently impossible to search for relevant research with precision. Better automated data extraction may not only facilitate the stage of review traditionally labelled 'data extraction', but also change earlier phases of the review process by making it possible to identify relevant research.

The use of bibliography enriched features for automatic citation screening.

Context: Citation screening (also called study selection) is a phase of systematic review process that has attracted a growing interest on the use of text mining (TM) methods to support it to reduce time and effort. Search results are usually imbalanced between the relevant and the irrelevant classes of returned citations. Class imbalance among other factors has been a persistent problem that impairs the performance of TM models, particularly in the context of automatic citation screening for systematic reviews.

Reproducibility of studies on text mining for citation screening in systematic reviews: Evaluation and checklist.

Context: Independent validation of published scientific results through study replication is a pre-condition for accepting the validity of such results. In computation research, full replication is often unrealistic for independent results validation, therefore, study reproduction has been justified as the minimum acceptable standard to evaluate the validity of scientific claims. The application of text mining techniques to citation screening in the context of systematic literature reviews is a relatively young and growing computational field with high relevance for software engineering, medical research and other fields.