Evidence map›Paper›PMID 34922933›Full record

ArticleChest2022

Decision Support Tools for Low-Dose CT Lung Cancer Screening: A Scoping Review of Information Content, Format, and Presentation Methods.

Mbasan Jallow, Stefanie Bonfield, Clara Kurtidu, David R Baldwin, Georgia Black, Kate E Brain, Michael Donnelly, Samuel M Janes, Grace McCutchan, Kathryn A Robb and 3 more

Abstract readScoping Review
In one paragraph

Article in Chest, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Review
  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

13 authors.

Mbasan JallowResearch Department of Behavioural Science and Health, University College London, London, England.
Stefanie BonfieldResearch Department of Behavioural Science and Health, University College London, London, England; Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, England.
Clara KurtiduInstitute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland.
David R BaldwinDepartment of Respiratory Medicine, Nottingham University Hospitals NHS Trust, City Hospital, Nottingham, England.
Georgia BlackDepartment of Applied Health Research, University College London, London, England.
Kate E BrainDivision of Population Medicine, Cardiff University, Cardiff, Wales.
Michael DonnellyCentre for Public Health, Queen's University Belfast, Belfast, Northern Ireland.
Samuel M JanesLungs for Living Research Centre, UCL Respiratory, Division of Medicine, University College London, London, England.
Grace McCutchanDivision of Population Medicine, Cardiff University, Cardiff, Wales.
Kathryn A RobbInstitute of Health and Wellbeing, University of Glasgow, Glasgow, Scotland.
Mamta RuparelLungs for Living Research Centre, UCL Respiratory, Division of Medicine, University College London, London, England.
Sandra Van OsDepartment of Applied Health Research, University College London, London, England.
Samantha L QuaifeResearch Department of Behavioural Science and Health, University College London, London, England; Wolfson Institute of Population Health, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, England. Electronic address: s.quaife@qmul.ac.uk.

Funding

Cancer Research UK 24460Cancer Research UK 30770
6 · The paper itself

Abstract

Several countries mandate informed or shared decision-making for low-dose CT (LDCT) lung cancer screening, but knowledge is limited about the type of information and presentation techniques used to support decision-making in practice. This review aimed to characterize the content, format, mode, and presentation methods of decision support tools (DSTs) for LDCT lung cancer screening. DSTs reported within peer-reviewed articles (January 2000-April 2021) were identified systematically from PubMed, PsycInfo, EMBASE, and CINAHL Plus. Inclusion criteria revolved around the development or evaluation of a resource or tool intended to support individual or shared decision-making for LDCT lung cancer screening. The data-charting and extraction framework was based on the International Patient Decision Aids Standards instrument and Template for Intervention Description and Reporting. Extracted data were organized within two categories: (1) study characteristics and context, format, and mode of DST use and (2) DST content and presentation methods. This review identified 22 DSTs in paper, video, or electronic formats across 26 articles. Most DSTs (n = 13) focused on knowledge exchange, whereas seven used interactive techniques to support values clarification (eg, Likert scales) and nine DSTs guided deliberation (eg, suggested discussion topics). The DSTs addressed similar topics, but the detail, quantification of probability, and presentation methods varied considerably. None described all the potential screening harms and results. The heterogeneity in DST design may affect the quality of decision-making, particularly for participants with lower literacy and numeracy. Evidence-based consensus guidelines for DST content and presentation methods should be developed collaboratively with screening-eligible adults.

Indexed as

Early Detection of CancerLung NeoplasmsAdultDecision MakingDecision Support TechniquesHumansTomography, X-Ray Computeddecision support toolshealth inequalityinformed decision-makinglung cancer screeningshared decision-making

Identifiers

PMID34922933
PMCPMC9562440

What OpenQuestion holds

Textmetadata
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.