Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews.

Background: Evidence-based medicine requires synthesis of research through rigorous and time-intensive systematic literature reviews (SLRs), with significant resource expenditure for data extraction from scientific publications. Machine learning may enable the timely completion of SLRs and reduce errors by automating data identification and extraction.

Methods: We evaluated the use of machine learning to extract data from publications related to SLRs in oncology (SLR 1) and Fabry disease (SLR 2).

Good Practice for Conference Abstracts and Presentations: GPCAP.

Research that has been sponsored by pharmaceutical, medical device and biotechnology companies is often presented at scientific and medical conferences. However, practices vary between organizations and it can be difficult to follow both individual conference requirements and good publication practice guidelines. Until now, no specific guidelines or recommendations have been available to describe best practice for conference presentations.

Commitments by the biopharmaceutical industry to clinical trial transparency: the evolving environment.

Clinical trial sponsors have ethical obligations to register protocols, report study results and comply with applicable legal requirements. To evaluate public commitments to trial disclosure and rates of disclosure by members and non-members of the European Federation of Pharmaceutical Industries and Associations (EFPIA) and/or the Pharmaceutical Research and Manufacturers of America (PhRMA). Websites of the top 50 biopharmaceutical companies by 2015 sales were searched for statements relating to trial data disclosure.