Gold standard (test) in the context of "Small intestinal bacterial overgrowth"

In medicine and statistics, sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a medical condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives:

• Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive.
• Specificity (true negative rate) is the probability of a negative test result, conditioned on the individual truly being negative.

If the true status of the condition cannot be known, sensitivity and specificity can be defined relative to a "gold standard test" which is assumed correct. For all testing, both diagnoses and screening, there is usually a trade-off between sensitivity and specificity, such that higher sensitivities will mean lower specificities and vice versa.

Hematoxylin and eosin stain (or haematoxylin and eosin stain or hematoxylin–eosin stain; often abbreviated as H&E stain or HE stain) is one of the principal tissue stains used in histology. It is the most widely used stain in medical diagnosis and is often the gold standard. For example, when a pathologist looks at a biopsy of a suspected cancer, the histological section is likely to be stained with H&E.

H&E is the combination of two histological stains: hematoxylin and eosin. The hematoxylin stains cell nuclei a purplish blue, and eosin stains the extracellular matrix and cytoplasm pink, with other structures taking on different shades, hues, and combinations of these colors. Hence a pathologist can easily differentiate between the nuclear and cytoplasmic parts of a cell, and additionally, the overall patterns of coloration from the stain show the general layout and distribution of cells and provides a general overview of a tissue sample's structure. Thus, pattern recognition, both by expert humans themselves and by software that aids those experts (in digital pathology), provides histologic information.