Evidence map›Paper›PMID 39995781›Full record

ReviewMDM policy & practice

How Time-Trend Tasks Are Affected by Probability Format: A Making Numbers Meaningful Systematic Review.

Mohit M Sharma, Jessica S Ancker, Natalie C Benda, Stephen B Johnson, Michelle Demetres, Diana Delgado, Brian J Zikmund-Fisher

Abstract readReview
In one paragraph

Review in MDM policy & practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

7 authors.

Mohit M SharmaDepartment of Population Health Sciences, Weill Cornell Medical College, New York, NY, USA.
Jessica S AnckerDepartment of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA.ORCID https://orcid.org/0000-0002-3859-9130
Natalie C BendaColumbia University School of Nursing, New York, NY, USA.ORCID https://orcid.org/0000-0002-3256-0243
Stephen B JohnsonDepartment of Population Health, New York University Langone Health, New York, NY, USA.
Michelle DemetresSamuel J. Wood Medical Library, Weill Cornell Medical College, New York, NY, USA.
Diana DelgadoSamuel J. Wood Medical Library, Weill Cornell Medical College, New York, NY, USA.
Brian J Zikmund-FisherDepartment of Health Behavior and Health Education, University of Michigan, Ann Arbor, MI, USA.ORCID https://orcid.org/0000-0002-1637-4176

Funding

Making Numbers Meaningful: Promoting Evidence-based Communication of Numbers in HealthR01LM012964 · NLM · WEILL MEDICAL COLL OF CORNELL UNIV · PI ANCKER, JESSICA S. · 2018 to 2021
$1.4M
NLM NIH HHS R01 LM012964
6 · The paper itself

Abstract

Highlights: This systematic review found that few studies of probability trend data compared similar formats or used comparable outcome measures.The only strong piece of evidence was that graphing probabilities over longer time periods such that the distance between curves widens will tend to increase the perceived difference between the curves.Weak evidence suggests that survival curves (versus mortality curves) may make it easier to identify the option with the highest overall survival.Weak evidence suggests that graphing probabilities over longer (rather than shorter) time periods may increase the ability to distinguish between small survival differences.Evidence was insufficient to determine whether any format influenced behaviors or behavioral intentions.

Indexed as

decision aidsevidence synthesisnumeracyrisk communicationrisk perceptionshared decision makingsystematic review

Identifiers

PMID39995781
PMCPMC11848886

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.