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• W2979408092 abstract "Fundamental scientific advances can take decades to translate into improvements in human health. Shortening this interval would increase the rate at which scientific discoveries lead to successful treatment of human disease. One way to accomplish this would be to identify which advances in knowledge are most likely to translate into clinical research. Toward that end, we built a machine learning system that detects whether a paper is likely to be cited by a future clinical trial or guideline. Despite the noisiness of citation dynamics, as little as 2 years of postpublication data yield accurate predictions about a paper’s eventual citation by a clinical article (accuracy = 84%, F1 score = 0.56; compared to 19% accuracy by chance). We found that distinct knowledge flow trajectories are linked to papers that either succeed or fail to influence clinical research. Translational progress in biomedicine can therefore be assessed and predicted in real time based on information conveyed by the scientific community’s early reaction to a paper." @default.
• W2979408092 created "2019-10-18" @default.
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• W2979408092 date "2019-10-10" @default.
• W2979408092 modified "2023-10-15" @default.
• W2979408092 title "Predicting translational progress in biomedical research" @default.
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• W2979408092 doi "https://doi.org/10.1371/journal.pbio.3000416" @default.
• W2979408092 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6786525" @default.
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