| 001 | 141708 | ||
| 005 | 20240229112525.0 | ||
| 024 | 7 | _ | |a 10.1136/gutjnl-2017-315730 |2 doi |
| 024 | 7 | _ | |a pmid:29615487 |2 pmid |
| 024 | 7 | _ | |a 0017-5749 |2 ISSN |
| 024 | 7 | _ | |a 1468-3288 |2 ISSN |
| 024 | 7 | _ | |a altmetric:35424777 |2 altmetric |
| 037 | _ | _ | |a DKFZ-2018-01979 |
| 041 | _ | _ | |a eng |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Smith, Todd |b 0 |
| 245 | _ | _ | |a Comparison of prognostic models to predict the occurrence of colorectal cancer in asymptomatic individuals: a systematic literature review and external validation in the EPIC and UK Biobank prospective cohort studies. |
| 260 | _ | _ | |a London |c 2019 |b BMJ Publishing Group |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1562232421_5829 |2 PUB:(DE-HGF) |x Review Article |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a To systematically identify and validate published colorectal cancer risk prediction models that do not require invasive testing in two large population-based prospective cohorts.Models were identified through an update of a published systematic review and validated in the European Prospective Investigation into Cancer and Nutrition (EPIC) and the UK Biobank. The performance of the models to predict the occurrence of colorectal cancer within 5 or 10 years after study enrolment was assessed by discrimination (C-statistic) and calibration (plots of observed vs predicted probability).The systematic review and its update identified 16 models from 8 publications (8 colorectal, 5 colon and 3 rectal). The number of participants included in each model validation ranged from 41 587 to 396 515, and the number of cases ranged from 115 to 1781. Eligible and ineligible participants across the models were largely comparable. Calibration of the models, where assessable, was very good and further improved by recalibration. The C-statistics of the models were largely similar between validation cohorts with the highest values achieved being 0.70 (95% CI 0.68 to 0.72) in the UK Biobank and 0.71 (95% CI 0.67 to 0.74) in EPIC.Several of these non-invasive models exhibited good calibration and discrimination within both external validation populations and are therefore potentially suitable candidates for the facilitation of risk stratification in population-based colorectal screening programmes. Future work should both evaluate this potential, through modelling and impact studies, and ascertain if further enhancement in their performance can be obtained. |
| 536 | _ | _ | |a 313 - Cancer risk factors and prevention (POF3-313) |0 G:(DE-HGF)POF3-313 |c POF3-313 |f POF III |x 0 |
| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 700 | 1 | _ | |a Muller, David C |b 1 |
| 700 | 1 | _ | |a Moons, Karel G M |b 2 |
| 700 | 1 | _ | |a Cross, Amanda J |b 3 |
| 700 | 1 | _ | |a Johansson, Mattias |b 4 |
| 700 | 1 | _ | |a Ferrari, Pietro |b 5 |
| 700 | 1 | _ | |a Fagherazzi, Guy |b 6 |
| 700 | 1 | _ | |a Peeters, Petra H M |b 7 |
| 700 | 1 | _ | |a Severi, Gianluca |b 8 |
| 700 | 1 | _ | |a Hüsing, Anika |0 P:(DE-He78)6519c85d61a3def7974665471b8a4f74 |b 9 |u dkfz |
| 700 | 1 | _ | |a Kaaks, Rudolf |0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a |b 10 |u dkfz |
| 700 | 1 | _ | |a Tjonneland, Anne |b 11 |
| 700 | 1 | _ | |a Olsen, Anja |b 12 |
| 700 | 1 | _ | |a Overvad, Kim |b 13 |
| 700 | 1 | _ | |a Bonet, Catalina |b 14 |
| 700 | 1 | _ | |a Rodriguez-Barranco, Miguel |b 15 |
| 700 | 1 | _ | |a Huerta, Jose Maria |b 16 |
| 700 | 1 | _ | |a Barricarte Gurrea, Aurelio |b 17 |
| 700 | 1 | _ | |a Bradbury, Kathryn E |0 0000-0003-3345-7333 |b 18 |
| 700 | 1 | _ | |a Trichopoulou, Antonia |b 19 |
| 700 | 1 | _ | |a Bamia, Christina |b 20 |
| 700 | 1 | _ | |a Orfanos, Philippos |b 21 |
| 700 | 1 | _ | |a Palli, Domenico |b 22 |
| 700 | 1 | _ | |a Pala, Valeria |b 23 |
| 700 | 1 | _ | |a Vineis, Paolo |b 24 |
| 700 | 1 | _ | |a Bueno-de-Mesquita, Bas |b 25 |
| 700 | 1 | _ | |a Ohlsson, Bodil |b 26 |
| 700 | 1 | _ | |a Harlid, Sophia |b 27 |
| 700 | 1 | _ | |a Van Guelpen, Bethany |b 28 |
| 700 | 1 | _ | |a Skeie, Guri |b 29 |
| 700 | 1 | _ | |a Weiderpass, Elisabete |b 30 |
| 700 | 1 | _ | |a Jenab, Mazda |b 31 |
| 700 | 1 | _ | |a Murphy, Neil |0 0000-0003-3347-8249 |b 32 |
| 700 | 1 | _ | |a Riboli, Elio |b 33 |
| 700 | 1 | _ | |a Gunter, Marc J |b 34 |
| 700 | 1 | _ | |a Aleksandrova, Krasimira Jekova |b 35 |
| 700 | 1 | _ | |a Tzoulaki, Ioanna |b 36 |
| 773 | _ | _ | |a 10.1136/gutjnl-2017-315730 |g p. gutjnl-2017-315730 - |0 PERI:(DE-600)1492637-4 |n 4 |p 672-683 |t Gut |v 68 |y 2019 |x 1468-3288 |
| 909 | C | O | |p VDB |o oai:inrepo02.dkfz.de:141708 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 9 |6 P:(DE-He78)6519c85d61a3def7974665471b8a4f74 |
| 910 | 1 | _ | |a Deutsches Krebsforschungszentrum |0 I:(DE-588b)2036810-0 |k DKFZ |b 10 |6 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a |
| 913 | 1 | _ | |a DE-HGF |l Krebsforschung |1 G:(DE-HGF)POF3-310 |0 G:(DE-HGF)POF3-313 |2 G:(DE-HGF)POF3-300 |v Cancer risk factors and prevention |x 0 |4 G:(DE-HGF)POF |3 G:(DE-HGF)POF3 |b Gesundheit |
| 914 | 1 | _ | |y 2019 |
| 915 | _ | _ | |a Allianz-Lizenz |0 StatID:(DE-HGF)0410 |2 StatID |
| 915 | _ | _ | |a Nationallizenz |0 StatID:(DE-HGF)0420 |2 StatID |
| 915 | _ | _ | |a JCR |0 StatID:(DE-HGF)0100 |2 StatID |b GUT : 2017 |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0310 |2 StatID |b NCBI Molecular Biology Database |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0320 |2 StatID |b PubMed Central |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0600 |2 StatID |b Ebsco Academic Search |
| 915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b ASC |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0110 |2 StatID |b Science Citation Index |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |
| 915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0111 |2 StatID |b Science Citation Index Expanded |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1110 |2 StatID |b Current Contents - Clinical Medicine |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1030 |2 StatID |b Current Contents - Life Sciences |
| 915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)1050 |2 StatID |b BIOSIS Previews |
| 915 | _ | _ | |a IF >= 15 |0 StatID:(DE-HGF)9915 |2 StatID |b GUT : 2017 |
| 920 | 1 | _ | |0 I:(DE-He78)C020-20160331 |k C020 |l Epidemiologie von Krebserkrankungen |x 0 |
| 980 | _ | _ | |a journal |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-He78)C020-20160331 |
| 980 | _ | _ | |a UNRESTRICTED |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
|---|
guest ::