  The value of biomedical research--a $1.7 trillion annual investment--is
ultimately determined by its downstream, real-world impact. Current objective
predictors of impact rest on proxy, reductive metrics of dissemination, such as
paper citation rates, whose relation to real-world translation remains
unquantified. Here we sought to determine the comparative predictability of
future real-world translation--as indexed by inclusion in patents, guidelines
or policy documents--from complex models of the abstract-level content of
biomedical publications versus citations and publication meta-data alone. We
develop a suite of representational and discriminative mathematical models of
multi-scale publication data, quantifying predictive performance out-of-sample,
ahead-of-time, across major biomedical domains, using the entire corpus of
biomedical research captured by Microsoft Academic Graph from 1990 to 2019,
encompassing 43.3 million papers across all domains. We show that citations are
only moderately predictive of translational impact as judged by inclusion in
patents, guidelines, or policy documents. By contrast, high-dimensional models
of publication titles, abstracts and metadata exhibit high fidelity (AUROC >
0.9), generalise across time and thematic domain, and transfer to the task of
recognising papers of Nobel Laureates. The translational impact of a paper
indexed by inclusion in patents, guidelines, or policy documents can be
predicted--out-of-sample and ahead-of-time--with substantially higher fidelity
from complex models of its abstract-level content than from models of
publication meta-data or citation metrics. We argue that content-based models
of impact are superior in performance to conventional, citation-based measures,
and sustain a stronger evidence-based claim to the objective measurement of
translational potential.
