Evidence map›Paper›PMID 26456873›Full record

ArticleAmerican journal of preventive medicine2015

Evaluation of a Personalized, Web-Based Decision Aid for Lung Cancer Screening.

Yan Kwan Lau, Tanner J Caverly, Pianpian Cao, Sarah T Cherng, Mindy West, Charles Gaber, Douglas Arenberg, Rafael Meza

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in American journal of preventive medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 9 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing papers in PubMed, 9 pooled it
16.5field-weighted citation impact, top 1% of its field
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

53 citing papers in PubMed, 9 syntheses or guidelines pooled it, 95 citations in OpenAlex.

  1. Knowledge translation strategies to enhance lung cancer screening programme implementation: a systematic review and meta-analysis.European respiratory review : an official journal of the European Respiratory Society · 2025
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  19. Patient Perspectives on a Patient-Facing Tool for Lung Cancer Screening.Health expectations : an international journal of public participation in health care and health policy · 2024
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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

8 authors at 1 institution in 1 country.

Yan Kwan LauDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Tanner J CaverlyDivision of General Internal Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan; Center for Clinical Management Research, Veterans Affairs Ann Arbor Healthcare System, Ann Arbor, Michigan.
Pianpian CaoDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Sarah T CherngDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Mindy WestDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Charles GaberDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan.
Douglas ArenbergDivision of Pulmonary and Critical Medicine, Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, Michigan.
Rafael MezaDepartment of Epidemiology, School of Public Health, University of Michigan, Ann Arbor, Michigan. Electronic address: rmeza@umich.edu.
University of Michigan · US

Funding

XenograftP30CA046592 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI THEODORE S LAWRENCE · 1988 to 2026
$178.2M
CDX2 TUMOR SUPPRESSOR PATHWAY DEFECTS IN COLON CANCERR01CA082223 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI FEARON, ERIC R. · 1999 to 2015
$3.9M
NCI NIH HHS P30 CA046592NCI NIH HHS R01 CA082223
6 · The paper itself

Abstract

introductionInformed decision making has been highlighted as an important aspect of lung cancer screening programs. This study seeks to assess the efficacy of a web-based patient decision aid for lung cancer screening, www.shouldiscreen.com.

methodsA before-and-after study (August through December 2014) was conducted where participants navigated a web-based decision aid that provided information about low-dose computed tomography lung cancer screening. Using an established prediction model, the decision aid computed baseline lung cancer risk and an individual's chances of benefiting from, and risk of being harmed by, screening. Outcome measures included knowledge of lung cancer risk factors and lung cancer screening, decisional conflict, concordance, and acceptability of the decision aid. Data were collected from 60 participants who were current or former smokers, had no history of lung cancer, and had not received a chest computed tomographic scan in the previous year. Analysis took place in 2015.

resultsKnowledge increased after seeing the decision aid compared with before (p<0.001), whereas the score on the Decisional Conflict Scale decreased (p<0.001). Concordance between a participant's preference to screen and the U.S. Preventive Services Task Force recommendation improved after seeing the decision aid (p<0.001). Risk perceptions among the screen-ineligible group changed (n=49), contrary to those who were eligible (n=11). Ninety-seven percent of the participants reported that the decision aid was likely useful for lung cancer screening decision making.

conclusionsThe web-based decision aid should be a helpful resource for individuals considering lung cancer screening, as well as for practitioners and health systems with lung cancer screening programs.

Indexed as

Decision Support TechniquesEarly Detection of CancerInternetPatient ParticipationAgedFemaleHumansLung NeoplasmsMaleMiddle AgedPatient Acceptance of Health Care

Identifiers

PMID26456873
PMCPMC5544524
OpenAlexW2180734158

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.