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cbse equations Flashcards

1
Q

filtration fraction

A

FF = GFR / RBF
GFR is about 120
RPF is about 600 (renal plasma flow)

in healthy FF is about 20% of RPF

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2
Q

clearance

A

Cs + [urine concentration of S] x [urine flow rate] / [plasma concentration of S]

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3
Q

GFR can be obtained by ?

RPF?

A

clearance of creatine

clearance of PAH

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4
Q

absolute risk increase?

number needed to harm?

A

adverse event rate in control and experimental then subtract adverse in experimental from control

NNH = 1/ the absolute risk increase

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5
Q

sensitivity

A

people with the disease that test positive

want this number high to know picking up on diagnosis
higher = better at ruling out disease
SCREENING diseases

A / A+C
true positive / true positive + flase negative

rule OUT disease

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6
Q

specificity

A

true negative
without the disease who test negative

closer to 100 p = better at ruling in
low false positive
confirm after positive screen
SPIN

D/D+B
true negative/ true negative + false positive

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7
Q

type 1 error

A

shows a relationship tht does not really exist

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8
Q

type II error

A

study fails to show a relationship that does exist

beta error

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9
Q

Vd=

A

amount of drug given (IV)/ [drug] plasma

low Vd = intravascular space and large/ charged molecules + bound

medium = intrvascualr and extracellular

large = able to get to all tissues and including fat
- usually small and lipophilic molecules

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10
Q

clearance of a drug

A

0.7 X Vd/ t 1/2

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11
Q

1/2 life =

A

.7 X Vd / clearance

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12
Q

hardy weinberg equations

A

p+q=1
P^2 +2pq+q^2=1

p^2= frequency of homozygous for p

q^2 = freq for homozygous for q

2pq= frequency of heterygosity

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13
Q

positive skew graph in terms of mean median and mode

negative?

A

shifted to the left
tail to the right

mean > median
median > mode

mode > median
median >mean

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14
Q

case control study

A

w/ disease and w/out groups
then look back at it

look at some exposure risk

retrospetive and observational

purely observational
- no intervention

*used to identify risk factors for diseases

NOT causal -
yields an odds ratio!!

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15
Q

odds ratio

study design and equation?

A

AxD divided by BxC

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16
Q

cohort study

A

group of people that has something in common

compare them to group that have not had that exposure and then follow them

*purely observational
either retrospective or prospective

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17
Q

formula associated with cohort study

A

relative risk

or risk ratio

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18
Q

relative risk equation

A

A/A+B divided by C/C+D

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19
Q

cross sectional study

A

looks at a population at a point in time
(ex dx of COPD at a time)
or ask about a risk factor
- exposed to 2nd hand smoke on that day

known as PREVALENCE

observational study

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20
Q

what does cross sectional show

A

prevalence

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21
Q

controlled clinical trial

A

investigator intervenes
so not purely observational - there is intervention

controlled = placebo and then an experimental group

22
Q

randomized

A

limit bias

23
Q

double blinded

A

particpant or investigator doesnt know who gets what

24
Q

meta analysis

A

combines data from many studies together

increase statistical power

quality depends on quality of individual studies

25
Q

true positive location

A

upper left (A)

26
Q

false positive location

A

upper right (B)

27
Q

false negative location

A

lower left (C)

28
Q

true positive location

A

lower right (D)

29
Q

sensitivity equation

+ acronym

A

A/ A+C

true positives divided by everyone who has the disease (true positives plus the false negatives)

1- the false negative

PID
- positive in disease

30
Q

specificity

+ acronym

A

proportion without disease with a negative test results

true negative / true negative plus false positives

D/ (D+B)

1- false positive

NIH acronym

negative in health

31
Q

PPV

positive predictive value

A

A/ (A+B)

true positives / everyone that tested positive

32
Q

NPV

negative predictive value

A

D/ (C+D)

33
Q

PPV change with increasing prevalence

A

PPV increases with increases in prevalence of disease

so increase true positives and false negatives

34
Q

NPV change with increasing prevalence

A

increasing flase negatives

so decreasing the NPV

35
Q

low disease prevalence changes to PPV and NPV?

A

PPV decreases and NPV increases

36
Q

prevalence

A

of people with the disease / total population

certain amount with disease at a certain time

37
Q

incidence

A 
# of new cases diagnosed / total # of people at risk 
for that illness

people with the disease not used in this calculation

38
Q

low prevalence effect on relative risk and odds ration

A

in low prevalence situations

RR will EQUAL OR

39
Q

attributable risk equation

A

rlative risk equation but instead of dividing you subtract

A/(A+B) - C/(C+D)

40
Q

absolute risk reduction

A

looks at how much an intervetnion will reduce risk of disease

opposite attributable risk equation

so C/(C+D) - A/(A+B)

41
Q

number needed to treat

A

1/ absolute risk reduction

needed to treat to save a life or avoid a bad outcome

42
Q

Number needed to harm

A

1 divided by the attributable risk reduction

which is A/(A+B) - C/(C+D)

so 1 divided by that

43
Q

standard distribution curve / graph

% that fall within SD?

A 
68% fall within 1 SD
(34% +/-)
95% fall within 2 SD
(13.5% +/-)
99.7% fall within 3 SD
(2.35% +/-)

then .15%

44
Q

small p value?

A

more likely to be

less than 0.05 - can reject null and shows an association

45
Q

standard error of the mean?

A

standard deviation divided by / square root sample size (n)

46
Q

confidence interval

A

range from
[mean-Z(SEM)] to [mean+Z(SEM)]

Z is specific to confidence interval

47
Q

Z in confidence interval if
90% CI?
95% CI?
99% CI?

A

90% CI = 1.645
95% CI =1.96
99% CI = 2.57

48
Q

if CI crosses 0?

A

accept null hypothesis

49
Q

chi ^2 vs t test

A

t test looks at the means

chi-square looks at percentaes or proportions of categorical outcomes in 2 or more groups

50
Q

correlation coefficient that is perfect

A 
1= perfect 
0= none

greater than 0 = positive correlation

less than 0 = negative

equations (2 decks)

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