"true positive (TP): an imaging test is positive and the patient has the disease/condition"
"On a first pass, we don't assume some relationship between the test and the disease/condition, but we hope there will be some relationship between the test and the disease/condition, because otherwise the test would be worthless."
"true positive (TP): an imaging test is positive and the patient has the disease/condition"
"true positive (TP): an imaging test is positive and the patient has the disease/condition"
"false positive (FP): an imaging test is positive and the patient does not have the disease/condition"
"false positive (FP): an imaging test is positive and the patient does not have the disease/condition"
"true negative (TN): an imaging test is negative and the patient does not have the disease/condition"
"true negative (TN): an imaging test is negative and the patient does not have the disease/condition"
"false negative (FN): an imaging test is negative and the patient has the disease/condition"
"false negative (FN): an imaging test is negative and the patient has the disease/condition"
"For a given test and disease/condition, its sensitivity is how well it can be positive among all those with the condition. Therefore:"
"For a given test and disease/condition, its specificity is how well it can distinguish those with disease from those without. The test must not just fail to pick up a segment of the population (that might be poor sensitivity), it must distinguish those without the disease... the true negatives (TNs). Therefore:"
"SnNout: if a diagnostic test, characterised by high sensitivity (Sn), returns the negative value (N), then it excludes the diagnosis (out) 2-3."
"SpPin: if a diagnostic test, characterised by high specificity (Sp), returns the positive value (P), then it admits the diagnosis (in) 2-3."
"false positive (FP): an imaging test is positive and the patient does not have the disease/condition"
"true negative (TN): an imaging test is negative and the patient does not have the disease/condition"
"For a given test and disease/condition, its specificity is how well it can distinguish those with disease from those without. The test must not just fail to pick up a segment of the population (that might be poor sensitivity), it must distinguish those without the disease... the true negatives (TNs). Therefore:"
"For a given test and disease/condition, its specificity is how well it can distinguish those with disease from those without. The test must not just fail to pick up a segment of the population (that might be poor sensitivity), it must distinguish those without the disease... the true negatives (TNs). Therefore:"
"SpPin and SnNout rule"
"ROC curve: graphically displays a diagnostic system's trade-off between sensitivity and specificity"
Expected headings
"Sensitivity"
"Specificity"
"SpPin and SnNout rule"