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\n You have full access to this article via your institution.<\/p>\n
Nature\u2019s modelling shows that previously awarded NIH grants that could be at risk today include funding for a project investigating whether computed-tomography scans improve detection of lung cancer. <\/span>Credit: K H Fung\/Science Photo Library<\/span><\/p>\n In 2011, researchers discovered that, compared with radiography (X-rays), computed tomography (CT) scans improved detection of lung cancer, reducing deaths by one-fifth (The National Lung Screening Trial Research Team N. Engl. J. Med. 365, 395\u2013409; 2011).<\/p>\n Their study, which has been cited almost 10,000 times, was made possible by a US$57-million National Institutes of Health (NIH) grant that funded a research institute at the University of California, Los Angeles (UCLA). Without that grant, \u201cwe would not be able to identify what is still today the largest single way of reducing deaths from lung cancer\u201d, says lead author Denise Aberle, a radiology researcher at UCLA\u2019s David Geffen School of Medicine.<\/p>\n What research might be lost after the NIH\u2019s cuts? Nature trained a bot to find out<\/p>\n<\/p>\n Yet if the team was seeking grant funding now, the work might not have happened. That\u2019s the conclusion of an analysis by reporters and data scientists in the Nature Careers and Nature Index teams. They trained a machine-learning model on existing NIH grants that have been cancelled since Donald Trump began his second presidential term and applied it to previously awarded NIH grants, to see which ones would have been at risk of cancellation. The grant that funded the lung-cancer-screening study was among those at risk.<\/p>\n Some 5,000 grants worth around $4.5 billion have been frozen or cancelled so far, as Nature\u2019s news team has reported. Despite ongoing lawsuits, efforts to reinstate the cut funding remain an uphill battle. It is, of course, the US government\u2019s prerogative to decide its spending priorities, including directing funding towards some broad areas of science and away from others. But the fact that a credible simulation of the administration\u2019s current policies found that life-saving work, with huge societal and economic benefits, might have been cancelled shows the self-defeating folly of how those policies have been implemented \u2014 and the need for an urgent change of approach.<\/p>\n As of now, the NIH has not published any methodology or criteria for cancellation, and it has not announced how these decisions are being made. The agency has not replied to several requests from Nature to explain its methods. But we do know some things.<\/p>\n Exclusive: NIH to dismiss dozens of grant reviewers to align with Trump priorities<\/p>\n<\/p>\n We know, for example, that NIH grant reviewers are being replaced with people whose stated views are more aligned with the administration\u2019s priorities. And in March, The New York Times scanned US government websites and found directives to remove 200 words and phrases, among them \u2018equitable\u2019, \u2018pregnant people\u2019 and \u2018systemic\u2019 \u2014 such as in systemic racism \u2014 in line with executive orders issued by the White House.<\/p>\n In June, the investigative-journalism organization ProPublica spoke to a whistle-blower who explained how he used artificial intelligence to identify candidate grants for cancellation at the Department of Veterans Affairs. Independent researchers we have spoken to say that the NIH might have used this approach.<\/p>\n The Nature analysis used data from two sources to train its model: the NIH\u2019s database of publicly accessible grants, called RePORTER, and Grant Witness, a website that tracks cancelled grants.<\/p>\n The results are eye-popping. More than 1,000 grants, worth almost $5 billion, had a 60% or higher likelihood of cancellation. The research funded by these grants resulted in 50,000 papers, which together have attracted three million citations. The work likely to have been cancelled is not only in fields targeted by the Trump administration, such as transgender health or diversity, equity and inclusion programmes. In the model and at the NIH, they run the full gamut of science: from public-health programmes to basic research into genetics, immunology and cancer.<\/p>\n However the cancellations were decided on, the impact on science and on scientific careers will be huge. We will certainly never know what research \u2014 and public benefit \u2014 might have come from the billions of dollars of science that have now been lost.<\/p>\n Judge rules against NIH grant cuts \u2014 and calls them discriminatory<\/p>\n<\/p>\n But hammering through vast swathes of publicly funded research without consulting the people doing that science, without a clear rationale for decisions, methodology or technique, isn\u2019t in the interests of science, the US taxpayer or, indeed, the world at large. It is inexplicable and destructive. At the very least, decisions on individual grants should still be peer reviewed by specialists in the relevant field.<\/p>\n For many researchers in the United States, a practical response to the current situation might well be to attempt to adjust, to tick the right boxes, to avoid any words that might upset an algorithm and to hope that reason soon prevails again. But there is still a risk that this approach to funding science will become both normal \u2014 and contagious. For governments in other countries, the United States, as the world\u2019s pre-eminent science power, is showing that it is normal to interfere with grant-assessment panels. At that point, the integrity of science might well be gone for good.<\/p>\n That hasn\u2019t happened yet. But now we can see just how much is at risk. The US government \u2014 and governments elsewhere tempted to go down a similar route \u2014 should take note.<\/p>\n","protected":false},"excerpt":{"rendered":" You have full access to this article via your institution. Nature\u2019s modelling shows that previously awarded NIH grants that could be at risk today include funding for a project investigating whether computed-tomography scans improve detection of lung cancer. Credit: K H Fung\/Science Photo Library In 2011, researchers discovered that, compared with radiography (X-rays), computed tomography<\/p>\n","protected":false},"author":1,"featured_media":23695,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[58],"tags":[6278,562,585,2993,434,3141,572],"class_list":{"0":"post-23694","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-science","8":"tag-consequences","9":"tag-cuts","10":"tag-learning","11":"tag-machine","12":"tag-potential","13":"tag-research","14":"tag-reveals"},"_links":{"self":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts\/23694","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=23694"}],"version-history":[{"count":0,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/posts\/23694\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=\/wp\/v2\/media\/23695"}],"wp:attachment":[{"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=23694"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=23694"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/naijaglobalnews.org\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=23694"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}Destructive decisions<\/h2>\n