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A hypothetical ideal "gold standard" test has a sensitivity of 100% concerning the presence of the disease (it identifies all individuals with a well-defined disease process; it does not have any false-negative results) and a specificity of 100% (it does not falsely identify someone with a condition that does not have the condition; it does not have any false-positive results).
Whether a bacterium is sensitive or not is determined by visual inspection or automatic optical methods, after a period of incubation. [5] Broth dilution is considered the gold standard for phenotypic testing. [14] The lowest concentration of antibiotics that inhibits growth is considered the MIC. [5]
The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against the gold standard four times, but a single additional test against the gold standard that gave a poor result would imply a sensitivity of ...
McDonald criteria are the standard clinical case definition for MS and the 2010 version is regarded as the gold standard test ... The sensitivity of McDonald criteria ...
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
These studies can thus be subjected to verification bias. One method to limit verification bias in clinical studies is to perform gold standard testing in a random sample of study participants. [citation needed] In most situations, verification bias introduces a sensitivity estimate that is too high and a specificity that is too low. [3]
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In medical testing with binary classification, the diagnostic odds ratio (DOR) is a measure of the effectiveness of a diagnostic test. [1] It is defined as the ratio of the odds of the test being positive if the subject has a disease relative to the odds of the test being positive if the subject does not have the disease.