ArticleApplied clinical informatics2018
Numeracy and Understanding of Quantitative Aspects of Predictive Models: A Pilot Study.
Article in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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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.
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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.
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Who cites it
3 citing papers in PubMed.
- Cognitive readiness of nurses regarding artificial intelligence predictions: understanding through the dual lens of verbatim and gist knowledge.JAMIA open · 2026Article
- Assessing Data Adequacy for High Blood Pressure Clinical Decision Support: A Quantitative Analysis.Applied clinical informatics · 2021Article
- Preferences for Predictive Model Characteristics among People Living with Chronic Lung Disease: A Discrete Choice Experiment.Medical decision making : an international journal of the Society for Medical Decision Making · 2020Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
backgroundThe assessment of user preferences for performance characteristics of patient-oriented clinical prediction models is lacking. It is unknown if complex statistical aspects of prediction models are readily understandable by a general audience.
objectiveA pilot study was conducted among nonclinical audiences to determine the feasibility of interpreting statistical concepts that describe the performance of prediction models.
methodsWe conducted a cross-sectional electronic survey using the Amazon Mechanical Turk platform. The survey instrument included educational modules about predictive models, sensitivity, specificity, and confidence intervals (CIs). Follow-up questions tested participants' abilities to interpret these characteristics with both verbatim and gist knowledge. Objective and subjective numeracy were assessed using previously validated instruments. We also tested understanding of these concepts when embedded in a sample discrete choice experiment task to establish feasibility for future elicitation of preferences using a discrete choice experiment design. Multivariable linear regression was used to identify factors associated with correct interpretation of statistical concepts.
resultsAmong 534 respondents who answered all nine questions, the mean correct responses was 95.9% (95% CI, 93.8-97.4) for sensitivity, 93.1% (95% CI, 90.5-95.0) for specificity, and 86.6% (95% CI, 83.3-89.3) for CIs. Verbatim interpretation was high for all concepts, but significantly higher than gist only for CIs (
conclusionThese results suggest that a nonclinical audience can interpret quantitative performance measures of predictive models with very high accuracy. Future development of patient-facing clinical prediction models can feasibly incorporate patient preferences for model features into their development.
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Registered trials
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.