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Exam (elaborations)

Intro to Clinical Medicine: Exam 1 With Questions And 100% ALL SURE ANSWERS

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Intro to Clinical Medicine: Exam 1 With Questions And 100% ALL SURE ANSWERS

Institution
CLINICAL MEDICINE
Course
CLINICAL MEDICINE









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Institution
CLINICAL MEDICINE
Course
CLINICAL MEDICINE

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Uploaded on
October 23, 2024
Number of pages
10
Written in
2024/2025
Type
Exam (elaborations)
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Questions & answers

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JACKLINE



Intro to Clinical Medicine: Exam 1 With Questions And 100% ALL SURE ANSWERS



Terms in this set (268)


what does a diagnosis involve? a collection of facts/ data and followed by analysis of these facts/data

what is a negative finding? when something is not present (and should be)




soft = runny nose, cough, malaise
list examples of soft vs. hard symptoms?
hard = coughing up blood

what is the difference between diagnosis and differential diagnoses includes all of the original possible diagnoses. from this list, then choose
differential diagnosis? the most likely = this is your diagnosis

Diagnoses of potentially significant or lethal true
consequences must remain on the DD list until
excluded
(true or false)

Diagnoses of treatable/curable conditions do false



1/1
0

, Intro to Clinical Medicine: Exam 1
Probability is a measure of s__________ and s__________ sensitivity, specificity

= the amount positive in ALL patients with the sensitivity
disease even though the disease may be inactive

= specificity
- Positive in patients having the disease
- Designed to establish a diagnosis not to
exclude
- Positive test may confirm but negative test
does not rule out

when considering sensitivity, what values are false negatives
encountered?

What is measured by How often FALSE sensitivity
NEGATIVES are encountered?


Example: If 90% sensitivity for disease X


= then 10% of patients with disease X have a
negative test (false -)


(1 in 10 people are told they do NOT have the
disease when they really do)

what is measured by how often FALSE specificity
POSITIVES result?


- Example: 90% specificity for disease Y
= then 10% of test results suggesting disease Y
are NOT due to disease Y (false +)


(1 in 10 people are told they have Disease Y but
really do NOT have this (may have another
disease, not Y))




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0

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