{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T03:28:08Z","timestamp":1772162888506,"version":"3.50.1"},"reference-count":16,"publisher":"American Society for Microbiology","issue":"5","license":[{"start":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T00:00:00Z","timestamp":1587427200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2020,4,21]],"date-time":"2020-04-21T00:00:00Z","timestamp":1587427200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.asm.org\/non-commercial-tdm-license"}],"funder":[{"DOI":"10.13039\/501100000024","name":"Government of Canada | Canadian Institutes of Health Research","doi-asserted-by":"publisher","award":["Training Award"],"award-info":[{"award-number":["Training Award"]}],"id":[{"id":"10.13039\/501100000024","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000057","name":"HHS | NIH | National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["U54GM088558"],"award-info":[{"award-number":["U54GM088558"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.asm.org"],"crossmark-restriction":true},"short-container-title":["Antimicrob Agents Chemother"],"published-print":{"date-parts":[[2020,4,21]]},"abstract":"<jats:p>\n                    The rising rates of antibiotic resistance increasingly compromise empirical treatment. Knowing the antibiotic susceptibility of a pathogen\u2019s close genetic relative(s) may improve empirical antibiotic selection. Using genomic and phenotypic data for\n                    <jats:named-content content-type=\"genus-species\">Escherichia coli<\/jats:named-content>\n                    isolates from three separate clinically derived databases, we evaluated multiple genomic methods and statistical models for predicting antibiotic susceptibility, focusing on potentially rapidly available information, such as lineage or genetic distance from archived isolates.\n                  <\/jats:p>","DOI":"10.1128\/aac.02417-19","type":"journal-article","created":{"date-parts":[[2020,3,6]],"date-time":"2020-03-06T05:14:19Z","timestamp":1583471659000},"update-policy":"https:\/\/doi.org\/10.1128\/asmj-crossmark-policy-page","source":"Crossref","is-referenced-by-count":11,"title":["Using Genetic Distance from Archived Samples for the Prediction of Antibiotic Resistance in\n                    <i>Escherichia coli<\/i>"],"prefix":"10.1128","volume":"64","author":[{"given":"Derek R.","family":"MacFadden","sequence":"first","affiliation":[{"name":"Division of Infectious Diseases, University of Toronto, Toronto, Canada"},{"name":"Harvard T. 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