Clinical decision making

Question 1

Heuristic add want kind of bias

Answer 1

overestimates the probability of rare disease

Question 2

Bois funnel

Answer 2

breadth of diff dx refined over course of interaction
–patterns of bias

Question 3

ascertainment bias

Answer 3

thinking shaped by prior expectation (gender, race)

Question 4

confirmation bias

Answer 4

tendency to look for confirming evidence

Question 5

diagnosis momentum

Answer 5

diagnostic considerations get stickier as passed on between clinicans

Question 6

value induced bias

Answer 6

horrible to miss a brain tumor, must get a head CT
overestimate probability of an outcome based on value of outcome

Question 7

metacognition

Answer 7

think about how you think

Question 8

Which of the following statements about decision trees is NOT correct

A circle represents a chance node

b.
A triangle represents an outcome

c.
The sum of probabilities at a decision node must equal 1

d.
A square represents a decision node

Answer 8

Correct. In decision-tree analysis, squares represent decision nodes, circles represent chance nodes, triangles represent outcomes. At each chance node, you model the likelihood of an event occurring based on its probability. Therefore, the sum of the probabilities at a chance node must equal one. Decision nodes do not reflect probabilities and instead represent the decision you’re trying to model or analyze.

Question 9

Availability bias

Answer 9

overestimating the likelihood of a rare event because of recent, memorable, or vivid examples

Question 10

value-induced bias

Answer 10

assigning a higher or lower probability of occurrence to an event because of the value you place on the event.

Question 11

Representativeness

Answer 11

assigning an occurrence to a category because the characteristics of the occurrence closely match the category, while ignoring the baseline rate of the category.

Question 12

Which of the following statements is true?

Select one:

a.
The test has a specificity of 30%, which means that if the test is negative, there is a 30% chance the condition is absent

b.
The test has a negative predictive value of 70%, which means that if the condition is absent, there is a 70% chance the test will be negative

c.
The test has a positive predictive value of 75%, which means that if the test is positive, there is a 75% chance the condition is present

d.
The test is 90% sensitive, which means that if the test is positive, there is a 90% chance the condition is present

Answer 12

he test has a positive predictive value of 75%, which means that if the test is positive, there is a 75% chance the condition is present

Question 14

1-specificity?

Answer 14

False positive rate

Question 15

characterize the ability of a test to discriminate between presence and absence of a condition.

Answer 15

Receiver operating characteristic curve analysis

Question 16

“precision”

Answer 16

Positive Predictive Value

Question 17

Sensitivity

Answer 17

probability of a positive test given the presence of disease,

Question 18

positive likelihood ratio

Answer 18

how much to increase the probability of disease if the test is positive

Question 19

Specificity

Answer 19

probability of a negative test given absence of disease,

Question 20

One minus sensitivity

Answer 20

false negative rate (very high sensitivity will therefore have very low false negative rates.)

Question 21

Different types of mathematical models

Answer 21

deterministic– values determined by parameters set in model (model always performs same
stochastic (random)– Monte carlo simulation

Question 22

SPIN” (specific tests rule a condition IN, you can trust a Positive test) and “SNOUT” (sensitive tests rule a condition OUT, you can trust a Negative test)