The DOI matches its Crossref record; 139 of the 139 references it cites were found in a registry or an index.
Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers
Open accessAbstract
BACKGROUND: Making evidence-based decisions often requires comparison of two or more options. Research-based evidence may exist which quantifies how likely the outcomes are for each option. Understanding these numeric estimates improves patients' risk perception and leads to better informed decision making. This paper summarises current "best practices" in communication of evidence-based numeric outcomes for developers of patient decision aids (PtDAs) and other health communication tools. METHOD: An expert consensus group of fourteen researchers from North America, Europe, and Australasia identified eleven main issues in risk communication. Two experts for each issue wrote a "state of the art" summary of best evidence, drawing on the PtDA, health, psychological, and broader scientific literature. In addition, commonly used terms were defined and a set of guiding principles and key messages derived from the results. RESULTS: The eleven key components of risk communication were: 1) Presenting the chance an event will occur; 2) Presenting changes in numeric outcomes; 3) Outcome estimates for test and screening decisions; 4) Numeric estimates in context and with evaluative labels; 5) Conveying uncertainty; 6) Visual formats; 7) Tailoring estimates; 8) Formats for understanding outcomes over time; 9) Narrative methods for conveying the chance of an event; 10) Important skills for understanding numerical estimates; and 11) Interactive web-based formats. Guiding principles from the evidence summaries advise that risk communication formats should reflect the task required of the user, should always define a relevant reference class (i.e., denominator) over time, should aim to use a consistent format throughout documents, should avoid "1 in x" formats and variable denominators, consider the magnitude of numbers used and the possibility of format bias, and should take into account the numeracy and graph literacy of the audience. CONCLUSION: A substantial and rapidly expanding evidence base exists for risk communication. Developers of tools to facilitate evidence-based decision making should apply these principles to improve the quality of risk communication in practice.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
The Framing of Decisions and the Psychology of Choice
Tversky, A., & Kahneman, D. (1981) · Science · doi:10.1126/science.7455683 · Cited by 18,497 · cited 1×
“The framing of outcomes in terms of losses or gains has been shown to affect people’s choices [21].”
BackgroundEvidence based medicine: what it is and what it isn't
Sackett, D. L., Rosenberg, W. M. C., Gray, J. A. M., Haynes, R. B., & Richardson, W. S. (1996) · BMJ · doi:10.1136/bmj.312.7023.71 · Cited by 15,769
“Risk, Ambiguity, and the Savage Axioms.”
Ellsberg, D. (1961) · The Quarterly Journal of Economics · doi:10.2307/1884324 · Cited by 7,891
Decision aids for people facing health treatment or screening decisions
Stacey, D., Légaré, F., Lewis, K., Barry, M. J., Bennett, C. L., Eden, K. B., Holmes-Rovner, M., Llewellyn-Thomas, H., Lyddiatt, A., Thomson, R., et al. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001431.pub5 · Cited by 6,685
Self-citationYeh, M.-L. (2014) · Journal of Nursing Research · doi:10.1097/jnr.0000000000000065 · Cited by 3,829
The Cambridge Handbook of Expertise and Expert Performance
(2006) · Cambridge University Press eBooks · doi:10.1017/cbo9780511816796 · Cited by 3,158
The Polarizing Impact of Science Literacy and Numeracy on Perceived Climate Change Risks
Braman, D., Kahan, D. M., Peters, E. M., Wittlin, M., Slovic, P., Ouellette, L. L., & Mandel, G. N. (2012) · Scholarly Commons - George Washington University Law School (George Washington University) · Cited by 2,431
Self-citationDoes tailoring matter? Meta-analytic review of tailored print health behavior change interventions
Noar, S. M., Benac, C. N., & Harris, M. S. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.4.673 · Cited by 2,136
How to improve Bayesian reasoning without instruction: Frequency formats.
Gigerenzer, G., & Hoffrage, U. (1995) · Psychological Review · doi:10.1037//0033-295x.102.4.684 · Cited by 2,127
Recent developments in modeling preferences: Uncertainty and ambiguity
Camerer, C., & Weber, M. (1992) · Journal of Risk and Uncertainty · doi:10.1007/bf00122575 · Cited by 1,795
Cited by
Decision aids for people facing health treatment or screening decisions
Stacey, D., Légaré, F., Lewis, K., Barry, M. J., Bennett, C. L., Eden, K. B., Holmes-Rovner, M., Llewellyn-Thomas, H., Lyddiatt, A., Thomson, R., et al. (2017) · Cochrane Database of Systematic Reviews · doi:10.1002/14651858.cd001431.pub5 · Cited by 6,685
Self-citationUsing social and behavioural science to support COVID-19 pandemic response
Bavel, J. J. V., Baicker, K., Boggio, P. S., Capraro, V., Cichocka, A., Cikara, M., Crockett, M. J., Crum, A. J., Douglas, K. M., Druckman, J. N., et al. (2020) · Nature Human Behaviour · doi:10.1038/s41562-020-0884-z · Cited by 4,964
Self-citationApplications of machine learning to diagnosis and treatment of neurodegenerative diseases
Myszczynska, M. A., Ojamies, P. N., Lacoste, A. M. B., Neil, D., Saffari, A., Mead, R., Hautbergue, G. M., Holbrook, J. D., & Ferraiuolo, L. (2020) · Nature Reviews Neurology · doi:10.1038/s41582-020-0377-8 · Cited by 595
Presenting machine learning model information to clinical end users with model facts labels
Sendak, M. P., Gao, M., Brajer, N., & Balu, S. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0253-3 · Cited by 279
A review of PHR, EMR and EHR integration: A more personalized healthcare and public health policy
Heart, T., Ben-Assuli, O., & Shabtai, I. (2017) · Health Policy and Technology · doi:10.1016/j.hlpt.2016.08.002 · Cited by 251
Metadata
- Authors
- Trevena, L. J., Zikmund-Fisher, B. J., Edwards, A., Gaissmaier, W., Galesic, M., Han, P. K., King, J., Lawson, M. L., Linder, S. K., Lipkus, I., Ozanne, E., Peters, E., Timmermans, D., & Woloshin, S.
- Venue
- BMC Medical Informatics and Decision Making
- Published
- 2013-11
- Volume
- 13
- Issue
- S2
- Pages
- S7-S7
- ISSN
- 1472-6947 (ISSN-L)
- OpenAlex
- W2071677582
- Cited by
- 538
- References
- 139
- FWCI
- 52.54
- Type
- journal article
- Language
- en
- Open access
- gold
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers. https://app.cochat.ai/papers/10.1186/1472-6947-13-s2-s7
- The paper
- Trevena, L. J., Zikmund-Fisher, B. J., Edwards, A., Gaissmaier, W., Galesic, M., Han, P. K., King, J., Lawson, M. L., Linder, S. K., Lipkus, I., Ozanne, E., Peters, E., Timmermans, D., & Woloshin, S. (2013). Presenting quantitative information about decision outcomes: a risk communication primer for patient decision aid developers. BMC Medical Informatics and Decision Making. https://doi.org/10.1186/1472-6947-13-s2-s7
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.