DIAGNOSTIC PREDICTION RULES: PRINCIPLES, REQUIREMENTS, AND PITFALLS
Abstract
This article focuses on the background, principles, requirements, and pitfalls of diagnostic prediction rules. Useful rules can only be derived from methodologically valid data bases. By considering the possibilities and limitations of prediction rules, the author concludes that these rules may support medical decision making, but cannot be a substitute for clinical skills and solid pathophysiologic knowledge.
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It is essential that key details of how a prediction model was developed and validated be clearly reported to enable synthesis and critical appraisal of all relevant information.
“It is therefore essential that key details of how a prediction model was developed and validated be clearly reported to enable synthesis and critical appraisal of all relevant information.14,33–36”
From Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD): The TRIPOD Statement (2015) · cites this paper as [36]
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- Authors
- Knottnerus, J. A.
- Venue
- Primary Care: Clinics in Office Practice
- Publisher
- Elsevier BV
- Published
- 1995-06
- Volume
- 22
- Issue
- 2
- Pages
- 341-363
- ISSN
- 0095-4543 (ISSN-L)
- OpenAlex
- W2405220364
- Cited by
- 56
- References
- 24
- Citations by year
- 2024: 1 · 2022: 1 · 2019: 1 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 13 · 2014: 4 · 2013: 1 · 2012: 2
- FWCI
- 1.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Primary Care Clinics in Office Practice (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Family Practice › Clinical Reasoning and Diagnostic Skills
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- clinical decision rules, clinical decision-making, clinical judgment, evidence-based medicine
- MeSH terms
- Decision Support Techniques, Diagnosis, Humans, Outcome and Process Assessment, Health Care, Predictive Value of Tests, Primary Health Care, Multivariate Analysis, Logistic Models
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