I think so, yes.
I can't tell if this paper is work of genius or fundamentally wrong. 
Usually in these cases, the prior is right and the work is broken somewhere. Usually.
https://www.nature.com/articles/s41380-018-0263-6 …pic.twitter.com/rnajAHf6ws
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Yeah, that would be really stupid. I was assuming they were reporting 10 fold cv results or something.
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If true, my guess is that editors and reviewers weren't familiar with ML methods in general, or the need for rigorous validation methods when applying them. Many social scientists think that just using adjusted R2 is enough!
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already has a preprint reply pointing out its problemshttps://www.biorxiv.org/content/early/2018/10/18/446518 …
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