Evidence map›Paper›PMID 26071497›Full record

ArticleJournal of epidemiology and community health2015

Overdiagnosis in lung cancer screening: why modelling is essential.

Kevin Ten Haaf, Harry J de Koning

Abstract read
In one paragraph

Article in Journal of epidemiology and community health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
23citing papers in PubMed, 3 pooled it
–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

23 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Lung Cancer Screening with Low-Dose CT: a Meta-Analysis.Journal of general internal medicine · 2020
    Pooled it
  4. Article
  5. Article
  6. Article
  7. Article
  8. How will lung cancer screening and lung nodule management change the diagnostic and surgical lung cancer landscape?European respiratory review : an official journal of the European Respiratory Society · 2024
    Review
  9. Article
  10. Article
  11. Cost-Effectiveness of Surveillance Scanning Strategies after Curative Treatment of Non-Small-Cell Lung Cancer.Medical decision making : an international journal of the Society for Medical Decision Making · 2021
    Article
  12. Review
  13. Overdiagnosis in lung cancer screening.The Lancet. Respiratory medicine · 2021
    Article
  14. Article
  15. Article
  16. Article
  17. Article
  18. Recognising Lung Cancer in Primary Care.Advances in therapy · 2019
    Review
  19. Review
  20. 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

2 authors.

Kevin Ten HaafDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.
Harry J de KoningDepartment of Public Health, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands.

Funding

Comparative Modeling of Lung Cancer Prevention and Control PoliciesU01CA199284 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI DE KONING, HARRY J, HOLFORD, THEODORE R · 2015 to 2019
$8.4M
Comparative Modeling of Lung Cancer Control PoliciesU01CA152956 · NCI · MASSACHUSETTS GENERAL HOSPITAL · PI DE KONING, HARRY J, KONG, CHUNG YIN · 2010 to 2014
$7.4M
NCI NIH HHS 5U01CA152956-04NCI NIH HHS U01 CA152956NCI NIH HHS U01 CA199284
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

Medical OveruseAge FactorsBiasClinical Trials as TopicComputer SimulationEarly Detection of CancerFalse Positive ReactionsHumansLung NeoplasmsMass ScreeningModels, StatisticalRadiation ExposureRadiography, ThoracicTomography, X-Ray ComputedCANCERCancer epidemiologySCREENING

Identifiers

PMID26071497
PMCPMC4978857

What OpenQuestion holds

Textmetadata
LicenceTDM
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.