Systematic Review Search Strategy Development: (Very Nearly) A Thing of the Past?

A guest post by Rachel Pinotti, MLIS, AHIP Recently, a faculty member sent me a copy of a June 2017 editorial published in Annals of Internal Medicine entitled Computer-Aided Systematic Review Screening Comes of Age along with the article which it accompanied.  The editorial argues, in short, that machine learning algorithms generate superior results to human-designed search strategies.  It asks (and answers), “Is it time to abandon the dogma that no stone be left unturned when conducting literature searches for systematic reviews? We believe so, because it has a deleterious effect on the number and timeliness of updates and, ultimately, patient health.” (Hemens & Iorio, 2017) As a librarian who conducts, consults, and teaches systematic review searching, this unleashed a flood of thoughts and questions. On a philosophical level, these authors’ thesis raised a real tension that I feel with regards to so many topics I teach about: the tension between teaching students about the way things are now vs. the way they very likely will be in the near-to-medium term future. As of now, I don’t think GLMnet and GBM, the machine-learning algorithms utilized in the original article which the editorial accompanies (Shekelle, Shetty, Newberry, Maglione, & Motala, 2017) are widely utilized for systematic review searching, but they quite possibly may be in 3-5-7 years’ time (or less).  Are students better off learning to design and execute comprehensive search strate...
Source: The Krafty Librarian - Category: Databases & Libraries Authors: Tags: Uncategorized Source Type: blogs