Evidence map›Paper›PMID 33293425›Full record

ArticleCancer research2021

Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling.

Kit Curtius, Anup Dewanji, William D Hazelton, Joel H Rubenstein, Georg E Luebeck

Abstract readValidation Study
In one paragraph

Article in Cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Kit CurtiusCentre for Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University of London, London, United Kingdom. kcurtius@health.ucsd.edu.ORCID https://orcid.org/0000-0002-2678-0960
Anup DewanjiApplied Statistics Unit, Indian Statistical Institute, Kolkata, India.
William D HazeltonProgram in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington.
Joel H RubensteinDivision of Gastroenterology, University of Michigan Medical School, Ann Arbor, Michigan.
Georg E LuebeckProgram in Computational Biology, Fred Hutchinson Cancer Research Center, Seattle, Washington.ORCID https://orcid.org/0000-0002-4378-2829

Funding

Validation & Pathology CoreU54CA163059 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI RUBENSTEIN, JOEL H, WANG, THOMAS D · 2011 to 2022
$12.1M
Controlling Esophageal Cancer: A Collaborative Modeling ApproachU01CA199336 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HUR, CHIN, INADOMI, JOHN MATTHEW · 2015 to 2019
$5.9M
Controlling Esophageal Cancer in the US.: A Colloraborative Modeling ProjectU01CA152926 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI HUR, CHIN, INADOMI, JOHN MATTHEW · 2010 to 2014
$3.9M
Esophageal Cancer from Cells to Population: A Multiscale ApproachU01CA182940 · NCI · FRED HUTCHINSON CANCER RESEARCH CENTER · PI HUR, CHIN, INADOMI, JOHN MATTHEW · 2013 to 2017
$3.3M
Medical Research Council MR/S003851/1NCI NIH HHS U01 CA152926NCI NIH HHS U01 CA182940NCI NIH HHS U01 CA199336NCI NIH HHS U54 CA163059
6 · The paper itself

Abstract

Cancer screening and early detection efforts have been partially successful in reducing incidence and mortality, but many improvements are needed. Although current medical practice is informed by epidemiologic studies and experts, the decisions for guidelines are ultimately

Indexed as

Models, TheoreticalAdenocarcinomaAdultAgedAged, 80 and overBarrett EsophagusCalibrationClonal EvolutionCost-Benefit AnalysisDatasets as TopicEarly Detection of CancerEsophageal NeoplasmsFemaleGastroesophageal RefluxHumansIncidence

Identifiers

PMID33293425
PMCPMC7611607

What OpenQuestion holds

Textmetadata
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Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.