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1000 Titel
  • Risk prediction models versus simplified selection criteria to determine eligibility for lung cancer screening: an analysis of German federal-wide survey and incidence data
1000 Autor/in
  1. Hüsing, Anika |
  2. Kaaks, Rudolf |
1000 Erscheinungsjahr 2020
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2020-06-27
1000 Erschienen in
1000 Quellenangabe
  • 35(10):899-912
1000 Copyrightjahr
  • 2020
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1007/s10654-020-00657-w |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7524688/ |
1000 Publikationsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • As randomized trials in the USA and Europe have convincingly demonstrated efficacy of lung cancer screening by computed tomography (CT), European countries are discussing the introduction of screening programs. To maintain acceptable cost-benefit and clinical benefit-to-harm ratios, screening should be offered to individuals at sufficiently elevated risk of having lung cancer. Using federal-wide survey and lung cancer incidence data (2008-2013), we examined the performance of four well-established risk models from the USA (PLCO
1000 Sacherschließung
lokal Surveys and Questionnaires [MeSH]
lokal Female [MeSH]
lokal Early Detection of Cancer/methods [MeSH]
lokal Risk Assessment [MeSH]
lokal Adult [MeSH]
lokal Mass Screening/methods [MeSH]
lokal Risk models
lokal Humans [MeSH]
lokal Lung Neoplasms/diagnosis [MeSH]
lokal Patient Selection [MeSH]
lokal Smoking/epidemiology [MeSH]
lokal Incidence [MeSH]
lokal Predictive Value of Tests [MeSH]
lokal Middle Aged [MeSH]
lokal Risk Factors [MeSH]
lokal Smoking/adverse effects [MeSH]
lokal Lung Neoplasms/epidemiology [MeSH]
lokal Germany/epidemiology [MeSH]
lokal Tomography, X-Ray Computed [MeSH]
lokal Eligibility Determination [MeSH]
lokal Male [MeSH]
lokal Lung cancer
lokal Eligibility
lokal Screening
1000 Liste der Beteiligten
  1. https://frl.publisso.de/adhoc/uri/SMO8c2luZywgQW5pa2E=|https://frl.publisso.de/adhoc/uri/S2Fha3MsIFJ1ZG9sZg==
1000 Hinweis
  • DeepGreen-ID: 8b53ec0902324bdbbb4c229be4b550cd ; metadata provieded by: DeepGreen (https://www.oa-deepgreen.de/api/v1/), LIVIVO search scope life sciences (http://z3950.zbmed.de:6210/livivo), Crossref Unified Resource API (https://api.crossref.org/swagger-ui/index.html), to.science.api (https://frl.publisso.de/), ZDB JSON-API (beta) (https://zeitschriftendatenbank.de/api/), lobid - Dateninfrastruktur für Bibliotheken (https://lobid.org/resources/search)
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1000 Erstellt am 2023-11-17T16:12:32.655+0100
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1000 Zuletzt bearbeitet 2023-12-01T07:51:20.446+0100
1000 Objekt bearb. Fri Dec 01 07:51:20 CET 2023
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