EBP 2: week 1.3 diagnostic test-measurement properties

name the 6 diagnostic measures

1. reference

2. index

3. condition present

4. condition absent

5. test positive

6. test negative

diagnostic measure: reference

what is the gold standard?, define it

diagnostic measure: index

special test being evaluated, what are you looking at

diagnostic measure: condition present

is it present in the test/ gold standard, does the person have what is being evaluated ud

diagnostic measure: condition absent

absences of the reference test, person does not have gold standard/ conditioniagnosd

diagnostic measure: test positive

special test met threshold, index met iagnod

diagnostic measure: test negative

social test threshold not met, rep

prevalence

how common a condition or disease is in a population at a specific time alculac

prevalence calculation

condition present/ total population same as (TP+FN)/(TP+FN+TN+FP)erfep

perfect test

identifies all people who are positive with condition and all pf people without it (ensisTP and TN only)

sensitivity and specificity begin with what factor?

starts with condition, do they have it? ensitivitys

sensitivity

the percent of individuals with condition present and tested positive ens

sensitivity calculation

[(TP/TP+FN)x100]pecis

specificity

the percent go individuals with condition absent and tested negative pecifics

specificity calculation

[(TN/FP+TN)x100]

proportion of patients with the condition who have a positive test result

sensitivity

if test with high sensitivity have few false negatives, how would you interpret the screening test?

negative results rule out condition

proportion of patients without the condition who have a negative test result

specificity f o

if test with high specificity have few false positives, how would you interpret this confirmatory test?

postive results rule in the condition

positive and negative predictive value begins with what factor?

the results of the testosip

positive predictive value

the probability that given a positive test results an individual will have the condition

positive predictive value calculation

[(TP/TP+FP)x100]

negative predictive value

the probability that given a negative test result a patient will not have the condition rgn

negative predictive value calculation

[(TN/TN+FN)x100]

effects of a high prevalence value (condition is common)

• Positive predictive value increases

• negative predictive value decreases

• postive test become more believable

predictive values are dependent on what information?

• prevalence

• population

effects of a low prevalence value (condition is rare)

• Positive predictive value decreases

• negative predictive value increases

• positive test become less convincing