The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.
Assessing the Generalizability of Prognostic Information
Abstract
Physicians are often asked to make prognostic assessments but often worry that their assessments will prove inaccurate. Prognostic systems were developed to enhance the accuracy of such assessments. This paper describes an approach for evaluating prognostic systems based on the accuracy (calibration and discrimination) and generalizability (reproducibility and transportability) of the system's predictions. Reproducibility is the ability to produce accurate predictions among patients not included in the development of the system but from the same population. Transportability is the ability to produce accurate predictions among patients drawn from a different but plausibly related population. On the basis of the observation that the generalizability of a prognostic system is commonly limited to a single historical period, geographic location, methodologic approach, disease spectrum, or follow-up interval, we describe a working hierarchy of the cumulative generalizability of prognostic systems. This approach is illustrated in a structured review of the Dukes and Jass staging systems for colon and rectal cancer and applied to a young man with colon cancer. Because it treats the development of the system as a "black box" and evaluates only the performance of the predictions, the approach can be applied to any system that generates predicted probabilities. Although the Dukes and Jass staging systems are discrete, the approach can also be applied to systems that generate continuous predictions and, with some modification, to systems that predict over multiple time periods. Like any scientific hypothesis, the generalizability of a prognostic system is established by being tested and being found accurate across increasingly diverse settings. The more numerous and diverse the settings in which the system is tested and found accurate, the more likely it will generalize to an untested setting.
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“There is no external validity per se ; the term is meaningful only with regard to clearly specified conditions [ 217 ].”
From Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and Elaboration (2007) · cites this paper as [217]
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“This quality becomes crucial when predictive models are deployed across different hospitals or healthcare systems, where variations in patient demographics, clinical practices, and data collection procedures can lead to inaccurate predictions [17, 18].”
From Toward generalizable and interpretable machine learning models in healthcare: Insights from ICU outcome predictions (2026) · cites this paper as [17]
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Metadata
- Authors
- Justice, A. C., Covinsky, K. E., & Berlin, J. A.
- Affiliations
- San Francisco VA Medical Center (US)
- Corresponding author
- Justice, Amy C.
- ORCID
- Justice, Amy C.
- Venue
- Annals of Internal Medicine
- Publisher
- American College of Physicians
- Published
- 1999-03-16
- Volume
- 130
- Issue
- 6
- Pages
- 515-524
- ISSN
- 0003-4819 (ISSN-L)
- 1539-3704
- OpenAlex
- W2089463633
- Cited by
- 1,160
- References
- 59
- Citations by year
- 2026: 19 · 2025: 20 · 2024: 46 · 2023: 56 · 2022: 63 · 2021: 49 · 2020: 48 · 2019: 51 · 2018: 44 · 2017: 60 · 2016: 47 · 2015: 67 · 2014: 55 · 2013: 44 · 2012: 47
- FWCI
- 24.34
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Annals of Internal Medicine (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Agency for Healthcare Research and Quality (5 T32 HS00009)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Keywords
- prognostic systems, generalizability, reproducibility, transportability, calibration, discrimination (statistics), colon cancer, rectal cancer, Dukes staging, cancer staging systems, prediction accuracy, black-box evaluation
- MeSH terms
- Calibration, Colonic Neoplasms, Evaluation Studies as Topic, Follow-Up Studies, Humans, Male, Middle Aged, Neoplasm Staging, Prognosis, Rectal Neoplasms, Reproducibility of Results
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