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Importance: Current reports suggest that the surgeon-scientist phenotype is significantly threatened. However, a significant increase in the proportion of surgeons in the workforce funded by the National Institutes of Health (NIH) from 2010 (0.5%) to 2020 (0.7%) was recently reported and showed that surgeons primarily performed basic science research (78% in 2010; 73% in 2020) rather than clinical research., Objective: To provide an update on the status of surgeons funded by the NIH for fiscal year (FY) 2022., Evidence Review: NIH-funded surgeons were identified in FY2012 and FY2022, including those who were awarded grants with more than 1 principal investigator (PI) by querying the internal database at the NIH. The main outcome for this study was the total number of NIH-funded surgeons in FY2012 and FY2022, including total grant costs and number of grants. The secondary analysis included self-reported demographic characteristics of the surgeons in FY2022. The research type (basic science vs clinical) of R01 grants was also examined., Findings: Including multiple PI grants, 1324 surgeon-scientists were awarded $1.3 billion in FY2022. Women surgeons increased to 31.3% (339 of 1084) of the population of surgeon PIs in FY2022 compared with 21.0% (184 of 876) in FY2012. Among surgeon PIs awarded grants, a total of 200 (22.8%) were Asian, 35 (4.0%) were Black or African American, 18 (2.1%) were another race (including American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, and more than 1 race), and 623 (71.1%) were White. A total of 513 of 689 R01 grants (74.5%) were for basic science, 131 (19.0%) were for clinical trials, and 45 (6.5%) were for outcomes research., Conclusions and Relevance: NIH-funded surgeons are increasing in number and grant costs, including the proportion of women surgeon PIs, and are representative of the diversity among US academic surgical faculty. The results of this study suggest that despite the many obstacles surgeon-scientists face, their research portfolio continues to grow, they perform a myriad of mostly basic scientific research as both independent PIs and on multidisciplinary teams.

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., Competing Interests: Since the authors work in the Division of Program Coordination, Planning, and Strategic Initiatives at the National Institutes of Health, our work could have policy implications for how research portfolios are evaluated.

Despite efforts to promote diversity in the biomedical workforce, there remains a lower rate of funding of National Institutes of Health R01 applications submitted by African-American/black (AA/B) scientists relative to white scientists. To identify underlying causes of this funding gap, we analyzed six stages of the application process from 2011 to 2015 and found that disparate outcomes arise at three of the six: decision to discuss, impact score assignment, and a previously unstudied stage, topic choice. Notably, AA/B applicants tend to propose research on topics with lower award rates. These topics include research at the community and population level, as opposed to more fundamental and mechanistic investigations; the latter tend to have higher award rates. Topic choice alone accounts for over 20% of the funding gap after controlling for multiple variables, including the applicant's prior achievements. Our findings can be used to inform interventions designed to close the funding gap., (Copyright © 2019 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).)

Given the vast scale of the modern scientific enterprise, it can be difficult for scientists to make judgments about the work of others through careful analysis of the entirety of the relevant literature. This has led to a reliance on metrics that are mathematically flawed and insufficiently diverse to account for the variety of ways in which investigators contribute to scientific progress. An urgent, critical first step in solving this problem is replacing the Journal Impact Factor with an article-level alternative. The Relative Citation Ratio (RCR), a metric that was designed to serve in that capacity, measures the influence of each publication on its respective area of research. RCR can serve as one component of a multifaceted metric that provides an effective data-driven supplement to expert opinion. Developing validated methods that quantify scientific progress can help to optimize the management of research investments and accelerate the acquisition of knowledge that improves human health., (© 2017 Santangelo. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).)

Despite their recognized limitations, bibliometric assessments of scientific productivity have been widely adopted. We describe here an improved method to quantify the influence of a research article by making novel use of its co-citation network to field-normalize the number of citations it has received. Article citation rates are divided by an expected citation rate that is derived from performance of articles in the same field and benchmarked to a peer comparison group. The resulting Relative Citation Ratio is article level and field independent and provides an alternative to the invalid practice of using journal impact factors to identify influential papers. To illustrate one application of our method, we analyzed 88,835 articles published between 2003 and 2010 and found that the National Institutes of Health awardees who authored those papers occupy relatively stable positions of influence across all disciplines. We demonstrate that the values generated by this method strongly correlate with the opinions of subject matter experts in biomedical research and suggest that the same approach should be generally applicable to articles published in all areas of science. A beta version of iCite, our web tool for calculating Relative Citation Ratios of articles listed in PubMed, is available at https://icite.od.nih.gov., Competing Interests: Since the authors work in the Division of Program Coordination, Planning, and Strategic Initiatives at the National Institutes of Health, our work could have policy implications for how research portfolios are evaluated.