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    Selecting the appropriate diagnostic procedure depends, in part, on the clinician’s index of suspicion.
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    The threshold model for testing contains two decision points: when the index of suspicion is high enough to order a diagnostic procedure, and when the index of suspicion is so high that results from a procedure will not influence subsequent actions.
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    Two components of evaluating diagnostic procedures are sensitivity and specificity.
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    Sensitivity is a procedure’s ability to detect a disease if one is present.
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    Specificity is a procedure’s ability to give a negative result if no disease is present.
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    Two errors are possible: false-positive results occur when the procedure is positive but no disease is present; false-negative results occur when the procedure is negative but a disease is present.
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    Sensitivity and specificity must be combined with the clinician’s index of suspicion to properly interpret a procedure.
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    The 2 × 2 table method provides a simple way to use sensitivity and specificity to determine how to interpret the diagnostic procedure after it is done.
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    After sensitivity and specificity are applied to the clinician’s index of suspicion, the probability of a disease based on a positive test and the probability of no disease with a negative test can be found. They are the predictive values of a positive and negative test, respectively.
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    A likelihood ratio is the ratio of true-positives to false-positives; it is used with the prior odds of a disease (instead of the prior probability) to determine the odds after the test is done.
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    A decision tree may be used to find predictive values.
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    Bayes’ theorem gives the probability of one outcome, given that another outcome has occurred. It is another way to calculate predictive values.
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    A sensitive test is best to rule out a disease; a specific test is used to rule in a disease.
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    ROC (receiver operating characteristic) curves are used for diagnostic procedures that give a numerical result, rather than simply being positive and negative.
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    Decision analysis, often using decision trees, is an optimal way to model approaches to diagnosis or management.
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    Outcomes for the decision analysis may be costs, quality-of-life adjusted survival, or subjective utilities measuring how the patient values different outcomes.
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    The optimal decision from a decision tree may be analyzed to learn how sensitive the decision is to various assumptions regarding probabilities, costs, etc.
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    Decision analysis can be used to compare two or more alternative approaches to diagnosis or management (or both).
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    Decision analysis can be used to compare the timing for diagnostic testing.
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    Journal articles should not publish predictive values without reminding readers that these values depend on the prevalence or index of suspicion.

Presenting Problem 1

A 57-year-old man presents with a history of low back pain. The pain is aching in quality, persists at rest, and is made worse by bending and lifting. The pain has been getting progressively worse, and in the past 6 weeks has been awakening him at night. Within the past 10 days ...

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