Clinical decision making Flashcards

1
Q

Heuristic add want kind of bias

A

overestimates the probability of rare disease

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2
Q

Bois funnel

A

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

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3
Q

ascertainment bias

A

thinking shaped by prior expectation (gender, race)

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4
Q

confirmation bias

A

tendency to look for confirming evidence

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5
Q

diagnosis momentum

A

diagnostic considerations get stickier as passed on between clinicans

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6
Q

value induced bias

A

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

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7
Q

metacognition

A

think about how you think

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8
Q

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

A

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.

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9
Q

Availability bias

A

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

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10
Q

value-induced bias

A

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

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11
Q

Representativeness

A

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

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12
Q

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

A

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

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13
Q

1-specificity?

A

False positive rate

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14
Q

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

A

Receiver operating characteristic curve analysis

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15
Q

“precision”

A

Positive Predictive Value

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16
Q

Sensitivity

A

probability of a positive test given the presence of disease,

17
Q

positive likelihood ratio

A

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

18
Q

Specificity

A

probability of a negative test given absence of disease,

19
Q

One minus sensitivity

A

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

20
Q

Different types of mathematical models

A

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

21
Q

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)

A