Diagnostic Reasoning.¶
Kassirer, J. P. (1989). Diagnostic Reasoning. Annals of Internal Medicine, 110(11), 893-900.
Cited by¶
2 citations across 2 artifacts.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Preimage
- In forensic and diagnostic reasoning the preimage of a symptom is the set of conditions that can produce it: the differential diagnosis in medicine, the suspect list in forensics, the fault tree in engineering, with the hardness of the problem being exactly the cardinality of this preimage.
This sourceFrames diagnostic reasoning as generating and narrowing the set of conditions consistent with observed findings.
Supported in partVerified against the publisher's abstract
Kassirer's abstract supports that clinical diagnosis proceeds by evoking diagnostic hypotheses and checking findings against them, but says nothing of preimages, forensics, fault trees, or cardinality as difficulty.
“Probabilistic reasoning is especially useful in evoking diagnostic hypotheses and in assessing the significance of clinical findings and test results.”
- In forensic and diagnostic reasoning the preimage of a symptom is the set of conditions that can produce it: the differential diagnosis in medicine, the suspect list in forensics, the fault tree in engineering, with the hardness of the problem being exactly the cardinality of this preimage.
- Residual Analysis
- A good clinician does not dismiss an unexplained symptom as noise; they treat it as the fingerprint of something unmodelled and fit the next-best hypothesis to that residual (a co-infection, a second condition, a drug interaction).
This sourceDescribes differential diagnosis as iterative hypothesis refinement driven by the findings a leading hypothesis fails to explain.
Supported in partVerified against the publisher's abstract
The abstract supports hypothesis-driven diagnostic reasoning, including assessing findings for coherency against a model, but says nothing about residuals or fitting a next-best hypothesis.
“Causal reasoning builds a physiologic model and assesses a patient's findings for coherency and completeness against the model; it functions especially effectively in verification of diagnostic hypotheses.”
- A good clinician does not dismiss an unexplained symptom as noise; they treat it as the fingerprint of something unmodelled and fit the next-best hypothesis to that residual (a co-infection, a second condition, a drug interaction).
Verification¶
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