The sheer scale of the pandemic has caused a scurry among scientists world over to find a treatment or vaccine. The result: an influx of research papers — some credible, others not. By May, bioRxiv and medRxiv had over 3,000 preprint research papers on the coronavirus. That’s an abundance of material for mainstream news publications to choose from, as concerned readers show an appetite for scientific research.
But this has had unintended consequences. Upon greater scrutiny, many of the preprint papers were recalled or debunked over flawed data or methods. But by then, their findings had made it to the smartphone screens of commoners around the world. The MIT Press, an affiliate of the Massachusetts Institute of Technology in Cambridge, wants to fix this.
This week, MIT Press, in partnership with UC Berkeley, launched the
Rapid Reviews: COVID-19, an open-access, rapid-review journal that aims to quickly weed out disinformation and poor scientific method from an ocean of research papers. MIT Press says the traditional peer-review of a scientific journal could take four or more weeks to complete — an excruciatingly slow process in the current circumstances. Enter
Rapid Reviews. Think of it as the scientific equivalent of a quick TV show review.
“Preprints have been a tremendous boon for scientific communication, but they come with some dangers, as we’ve seen with some that have been based on faulty methods,” Nick Lindsay, director of journals at the MIT Press,
told the Stat News. “We want to debunk research that’s poor and elevate research that’s good.”
“Using artificial intelligence tools, a global team will identify promising scholarship in preprint repositories, commission expert peer reviews, and publish the results on an open-access platform in a completely transparent process,” MIT Press
said in a statement. Editor-in-Chief Stefano M. Bertozzi, professor of health policy and management and dean emeritus of the School of Public Health at the UC Berkeley, said COVIDScholar, an initiative of UC Berkeley and Lawrence Berkeley National Lab, will create unique AI/machine learning tools to support the project.
