EBM Flashcards

1
Q

What is pellegra and what did it prove for using scientific method?

A

Symptoms are the 4D’s: diarrhea, dermatitis, dementia and death.

Originally attributed to a microbial infection. Goldberger suggested it was a dietary problem. Victims didn’t eat vegetables, meat, and milk. He did a bunch of tests and they kept getting rejected.

It was demonstrated that niacin (vitamin B3) deficiency caused pellagra.

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2
Q

What is H pylori

A

Infection by this bacteria causes ulcers. They were met with ridicule. They infected themselves and showed that the bacteria caused ulcers.

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3
Q

What is EBM

A

its the conscientous, explicit, and judicious use of the current best evidence in influencing decisions about the care of individual patients.

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4
Q

What is RCT (randomized control trial?)

A

It means taking your sample and randomnly assigning a control and what your testing.

Bradford hill (waksman) discovered the treatment for strep throat, streptomycin.

Archie Cochrane said treatments are only effective if they do more good than harm via RCT

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5
Q

GIve an example of each.

Diagnostic question,
prognostic question,
treatment question
prevention question

A
  • does my patient possibly have this diagnosis? what is abnormal? Should I perform a test and what are the risks?
  • what should this patient expect following treatment, surgery, etc..
  • what is the best treatment for the patient and which treatment has the lowest risk of harm?
  • what can I or the patient do to prevent these conditions.
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6
Q

What is a population and what is a sample, difference.

A

A population is all the people in a setting with certain defined characteristics that is of interest. The sample are subjects from the population.

You are trying to make inferences from the sample about the population.

A representative sample looks like the population and a biased sample does not (lower external validity)

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7
Q

Exclusion vs inclusion criteria

A

Inclusion criteria identify who will be in the study and exclusion criteria identify who will not.

Controls variability (better internal validity) but limits generalizability (external validity)

Inclusion: 30-50 year olds who are either coffee drinkers or non coffee drinkers.

Exclusion: smokers.

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8
Q

Probability sampling: when each member of the population has an equal chance of being included in the research. Describe the three kinds of probability sampling.

A

Simple random sampling: the most common form of probability sampling
-every person in the population has a random chance of being selected.

Stratified random sampling: to ensure variables are expressed. Each trait is then randomnly sampled.
Ex. if you just randomly sample school age children, you may just end up with a biased result of kindergartners. SO you take kindergarten, 1st grade, 2nd grade and randomly sample from each.

Systematic sampling - where patient data is ordered and you set a parameter for the selection of the data of interest. Every 10th data.

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9
Q

What are three non-probability sampling. What are some problems?

A

Purposive sampling - you are picking patients that will likely support your hypothesis. Really biased sampling.

Convenience sampling - you are choosing patients based on their availability for your study, asking people to volunteer for studies and taking that as your sample. There may be external factors, like poor people may join for money.

Quota sampling: like convenience sampling but includes prereqs. You would have an ask to volunteer but include parameters to get a representative sample of wht you want.

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10
Q

What are extraneous variables? and examples?

A

Variables in the study that may effect the relationship between the independent and dependent variables. Diet, amount of sleep, medications.

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11
Q

What is selection bias and how is it different from sampling bias?

What is measurement bias

Selection bias, extraneous factors, measurement bias can all confound your study. What is confounding?

A

Selection bias occurs when comparisons are made between nonequivalent groups. Ex. when the control group and treatment group are littered with extraneous variables that are not standardized. The hospital procedures, etc.

Measurement bias occurs when the methods of measurements differ between groups.

Confounding: an extraneous variable correlates with both the dependent and independent variable. You are allowing a third variable to influence the effects.

Selection bias is about the groups you assign within the sample.
Sampling bias is about your selection from the population.

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12
Q

What is the Central Limit Theorem

A

If you have a normal distribution (with a mean and a random, independent variable) and your sample is large enough, you can be 95% confident that it will match the population distribution.

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13
Q

What is the sample distribution? (mention SD’s) and variables m and s

A

if your sample size is large enough, the sample distribution will be bell shaped.
One standard deviation includes (34.1 x 2), 68.2 percent of your data. Two standard deviations includes about 95%
s=standard deviation
m = population mean.

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14
Q

Parametric vs non parametric

A

Parametric means normal distribution, you assume a mean and a standard deviation.

Non parametric makes no assumptions about the shape or form of the probability distribution. We’ll see data tends to be a normal distribution.

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15
Q

Describe a chi square statistic

A

It is the only test for studies using categories (no measurement, just number of cases that fall under yes or no) It is non parametric, it uses categories, you compare the observed numbers to the expected numbers to calculate your chi square statistic. The larger it is, the more related they are. Null hypothesis is rejected.

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16
Q

What is the method of a chi square statistic?

A

To calculate expected. You take one box, multiple the total in the row with the total in the column divided by the total for each box.

Then its ((observed - expected)^2)/expected and sum all the values. If your chi square statistic is larger than the critical value accounting degree of freedom, then your categories are related.

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17
Q

How does a case control and a retrospective cohort differ? Case control study vs cohort study.

A

The sampling is what is different. Case control, you would identify people with the condition.

Retrospective cohort would first pick a group that were related to each other (like a class of juniors) then identify from among them the condition. And gather information about their life

Cohort studies measure how many people develop disease out of a total. Incidence in both groups

Case control studies look at people who already have the disease and determine the odds that the diseased group was exposed.

Case-control is you take people who have the condition and you look into the past and ask if they were exposed to something.

Calculated by (those with the disease who were exposed/ those who have the disease and weren’t) / (those who don’t have the disease and were exposed/ those who don’t have the disease and weren’t exposed)

Cohort is a group of people who have something in common and then observed for a period of time. (people all have propensity for breast cancer) they differ in the variable of interest. Study a “predictor variable” (something that will likely lead to a condition)
Cohort studies look at how many people develop the disease out of the total. It looks at relative incidence of disease. Not good for chronic diseases (super long studies), not plausible experiments, incidence of head trauma on bike accidents wearing and not wearing helmet, Can’t make them do that.

You calculate a risk ratio. Incidence of disease in those exposed and incidence in those not exposed (the baseline).

Ex. 20 in every 100 minors develop COPD. 4 in every 100 surface workers develop COPD.
(20/100) / (4/100) = 5. Five times as likely to get COPD. If CI includes 1, no effect.

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18
Q

What happens if CI includes 1?

A

It has no effect, CI can not include 1.

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19
Q

Describe three kinds of sampling bias.

A

Healthy- user bias: select participants that are health conscious

Berkson’s bias - select a population from an impaired or diseased group

Exclusion bias - excluding participants who have a certain characteristic.

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20
Q

What is investigator bias and how is it avoided.

A

Investigator knows the expected results and treats the groups differently, influencing the results.

Allocation concealment: people do no know which group subjects are in. Random, and subjects given numbers

Investigator blinding: do not know which group they are providing the treatment to (don’t know what the treatment is) and taking measurements to.

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21
Q

What is the hawthorne effect? What is double blinding?

What is recall bias?

A

People change their behavior in a study, they want to please and perform as expected. Make sure subjects don’t know which group they are in and investigator is blinded. But if there is an obvious change this doesn’t really work.

Patients and the researchers are blinded.

Single blinding usually just refers to participants being blinded.

Recall bias is a threat to internal validity of a case control study. People can not remember the exposure or event of interest.

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22
Q

what is absolute risk. difference between odds ratio and relative risk

A

incidence is the number of new cases over a period of time.

Absolute risk is the number of people with the condition/total number of people.

Odds ratio:
Exposed vs not exposed for both scenarios

People with the disease, exposed over not exposed
People without the disease, exposed over not exposed
A/C // B/D

Risk ratio makes more sense.

Incidence in people who are exposed over incidence in people who are not exposed.
Exposure compared to baseline
A/(A+B) vs (C/C+D) exposed vs not exposed.

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23
Q

Ranks the hierarchy of evidence.

A

Systematic review>RCT>Cohort>Case control.

Systematic review is a summary of all the research out there.

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24
Q

Describe RAndomized Control study and what to look out for.

A

THe best way to measure cause and effect. You sample a group from a population then randomly assign people to experimental or control groups. (Random assignment - something that can’t be done in observational studies)

First concern, getting your population of interest. You don’t want a sampling bias. You may want exclusion criteria to just specify to your population of interest. You want to minimize differences within your sample that may interfere with your conclusion (socioeconomic status, occupation, etc)

IN the example they did a run in experiment. It is a small experiment you do before starting your real one to exclude people that would mess up your data. In this case it was compliance of taking aspirin.

Random assignment is important because you hope you equally distribute extraneous factors into each group so they end up washing out. You could you stratified randomization where you take groups with a known potential variable and randomnly assign them to each group. You just want to remove selection bias.

Control for extraneous factors. You want to check because even random could result in inherent biases. There were no differences between the groups in terms of smoking, incidence of diabetes, age..etc.

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25
Q

Two ways to deal with drop outs or treatment changes?

A

Intention to treat: you include data on those who switched or dropped out but still keep their original group assignment. You want to increase variability to increase external validity.

According to treatment received: you ignore those who dropped out and for those who switched treatments, people will belong to groups that they were in when the study ended.

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26
Q

How does observational differ from experimental?

A

No random assignment, can’t manipulate independent variables, hard to exclude all extraneous factors.

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27
Q

What is risk factor exposure? And what are ways to quantify it?

A

Exposure means the person, before becoming ill, has come in contact with the factor in question. So on the charts, its people who develop the disease and don’t develop the disease.

Ever been exposed, current dose, largest dose ever taken, cumulative dose, years of exposure.

Dose response curve are different based on the factor of interest. For smoking you would do pack years and a total cumulative does. Radiation event may just be ever exposed.

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28
Q

What are the 6 kinds of environmental risk factors?

A

Chemical agents: workplace exposure, cleaning products, industrial chemicals, pesticides, environmental smoke.

Physical agents: radiation, noise, vibration. (waves..)

Biologic agents: infectious agents, bacteria virus, allergens)

Psychosocial agents: depression stress, trauma

Mechanical agents: bodily harm from physical exertions, heavy lifting, repetitive motions like texting, injuries.

Life style risk factors (behavioral) = smoking, taking drugs, sun exposure, unsafe sex.

(biological, chemical, mechanical, physical) look like immediate causes

(psychosocial, lifestyle) seem like distant causes, more remote causes (factors related to education, less prenatal care, malnutrition)

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29
Q

WHat is latency?

A

The time between exposure to event. Most conditions that lead to morbidity have a long latency so the cause is hard to deduce.

Immediate, days, years, or decades.

Lung cancer from smoking is 25-30 years.

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30
Q

Common exposure to risk factors made it hard to distinguish an effect they were having as was the case for smoking.

A

It is only by comparing patterns of disease with and without these risk factors (control) could you deduce. It would be most useful if you took a group of people with the same propensity of getting a disease and then exposed one group to a risk factor and the other not. Unethical. You would have to do observational. See how the disease comes up and draw conclusions.

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31
Q

Low incidence and small risk. Multiple causes and effect.

A

Many diseases have low incidence 2- 3%. Even if it increases risk by 20% for example, incidence is still tiny. this makes it harder to also experiment based on such rare diseases.

Small risk: refers to how the effects of individual factors for chronic diseases are small. Because the effect is so small, you need to study a large number of people to detect the differences and it will be harder to even figure out a connection and start testing it.

Most diseases are not one to one. one factor can influence many diseases while one disease may have many causes.

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32
Q

What does it mean risk factor may not be causal.

A

Schizophrenia doesn’t cause lung cancer.

Those with schizo are more likely to develop lung cancer

This is because they smoke way more.

Schizophrenia is a marker for increased likelyhood for lung cancer but is indirectly connected.

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33
Q

Additive and multiplicative effects?

A

Some risk factors may add onto each other for increasing the probability of getting a condition. BUt sometimes it is multiplicative and exponentially increases your risk. As is smoking ad asbestos on lung cancer.

50/total got lung cancer when smoking and asbestos worker. 10/total got lung cancer only through smoking. 5 times more likely. Total 50 times more likely that a non smoker (1/ total).

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34
Q

Predicting risk

A

There are risk prediction tools like the Framingham risk score for cardiovascular disease. Used by doctors for how they treat a condition.

You enter the risk factors and then it will calculate the likelihood of getting a disease or event. Someone with a 25% chance will be treated more rigorously or earlier than that with a 2% chance.

35
Q

John snow and cholera.

A

He mapped the incidences of cholera throughout the city. Found a large amount of them occured near the broad st water pump. Ordered that to be removed and the cholera epidemic ended.

36
Q

Absolute risk, attributable risk, relative risk. Relative risk reduction Absolute risk reduction, NUmber needed to treat.

A

Absolute risk: what is the incidence of a condition in a group of people who didn’t previously have the condition.

Attributable risk (risk difference) - What is the difference in the rate of disease between exposed and unexposed (subtraction) 
"What is the additional risk of getting a disease if I am exposed"
Relative risk (risk ratio) - how many times more likely is the exposed group likely to develop the disease compared to not exposed. (division) .
"how many times more likely will an exposed person get the disease relative to nonexposed people. 

Relative risk reduction is how much the treatment reduced the risk of a bad outcome compared to the control group.

Absolute risk reduction is like risk difference: instead of exposed vs not exposed. It compares the effect of a treatment to untreated. 20 percent in untreated compared to 12 percent in treated. ARR = 8%.

NNT: 100 / ARR. The number needed to treat in order to prevent on case. 100 / 8 = 12.5. 13 needed to be treated to have one child benefit.

37
Q

Treatment: How would you calculate the following?

Control event rate
Experimental event rate
Relative risk
Relative risk reduction
Absolute risk
Number needed to treat 
Number needed to harm. 
                  yes      no exposed          a.       b

not exposed. c. d

A

CER: People who get the condition but not expose/Everyone not exposed to treatment c/c+d

EER: People who get the condition and are exposed/ everyone who was exposed
a/a+b

RR: EER/CER (if its small it will have a benefit) incidence in exposed/incidence in unexposed

ARR: CER - EER

RRR: (CER - EER)/ CER (compared to control group) Make your life easier:
1-RR.

NNT: 1/ ARR. (most intuitive statistic in clinical practice) if ARR is expressed as a decimal. 100/ARR if its percentage.

NNH is also 1/ARR. You use one over the other based on if a drug is reducing the number or increasing the number of an adverse condition. How many people need to receive the drug before something bad happens.

Then compare NNT vs NNH. One out of 33 people will benefit, one out of 50 will develop lung cancer.

38
Q

ARR makes risks or benefits looks smaller

Relative risk makes risk look bigger.

A

6%-3% - 3% absolute risk reduction

3/6 - 50% reduction.

39
Q

Quantifying the benefit.

MI risk is decreased to 10% compared to 15%

Calculate RR and Absolute Risk

A

10/15 = 66% Because it is a ratio it really depends. a 3% to 2% and a 75% to 50% are all .66 but have very different implications. RR is not a good measure of effect size. SInce RRR is just 1-RR it is also constant no matter effect size. So for .66, risk was 34% lower as compared to 5% lower.

15-10 = 5% risk reduction. You can also calculate NNT from that and both of these are more dependent on the effect size. The larger the absolute reduction, the fewer needed to treat.

40
Q

NNT expression practice

A

AFter you get NNT, it matters the type of treatment we are talking about. If you need 100 but the treatment is sunscreen, you may be willing to expose people to something relatively harmless to prevent one case. Chemo is a really hard process and only 1 out of 4 will get better after 3 years of chemo. Think hard nigga.

41
Q

What does standard error give us

A

It measures roughly the average difference between the statistic and the population parameter

Is the Estimated the standard deviation of the sampling distribution (sd/N)

Standard error can never be a negative number

42
Q

Type 1 error vs type 2 error

A

Type 1: get on with it. The null hypothesis is true but u reject it anyways. Happens from running multiple t tests.
Anova controls for this by keeping chance of type 1 error at 5%

Type 2: you’re an idiot. Your accept the null hypothesis even though u have somethingg significant

43
Q

Left and right skewed data sets

A

Left skewed: Mode and median are greater than mean.

right skewed: mode and median are less than mean

44
Q

What is three standard deviations?

A

99 percent. For concern on only one end, .5%

45
Q

How to calculate power from knowing type 1 and type 2 error rates?

A

Type 1 error rate is always set at 5 percent. .05 like p. Traditionally power is always 80. You look at the risk of making type 2 error which is beta. A 20 percent chance would be 1-.2 = .8

46
Q

Describe how the cut off you choose will effect test results. there is always a little overlap between those who don’t have the disease and those who do.

A

So there is a cutoff for your tests: Those who test negative and those who test positive. THose who are positive and are cut off to be in the test negative, will be false negatives. Those who don’t have the disease but test positive are false postives.

Negatives and postiives refer to test results. Disease is just yes or no.

47
Q

Calculate sensitivity, specificity, and overall accuracy:

             yes.       no  Positive     a.           b

Negative c. d

A

Sensitivity: true positives/all who have the disease a/a+c
the probability of testing positive if the patient has the disease.

Specificity: true negatives/ all who don’t have disease
d/(b+d)
probability of testing negative for those who don’t have the disease .

Accuracy: all trues/ total
a+d/a+b+c+d

all of these refer to application to diseased population. Dependent on whether someone has the disease or not.

48
Q

What is prevalence?
Positive predictive value
Negative predictive value

how is it different from sensitivity and specificity

A

prevalence: the probability of having a disease, proportion of people have the condition at a given point int time. (a+C)/total

Positive predictive value: probability of having the disease given a positive test result
a/(a+b) true positives/ all positives

Negative predictive value: probability of not having the disease given a negative test
d/(c+d) true negatives/ all negatives

Sensitivity/specificity: all who have the disease, who tested positive/all who don’t have the disease, who tested negative

From a disease standpoint

Predictive values: all who tested positive, who has the disease/ all who tested negative, who doesn’t have the disease

From a test result standpoint

49
Q

Describe bayes theorem - conditional probability

Normogram

A

Its the tables that take into account sensitivity, specificity and prevalence and then using the numbers to calculate PPV, NPV.
PPV = 8%
Out of those who test positive, the chance of actually having the disease is 8%

Normogram: you need the prevalence and likelihood ratio

Positive likelihood ratio (sensitivity/ 1- specificity)

Negative likelihood ratio (1-sensitivity)/specificity.

The larger or smaller your LR, the greater you PPV or NPV. Its better. For the normogram, you find your prevalence, then you use your LR+ to get line up to a PPV. Then you use your LR- to line up to the value. Difference is you do 1-That value to get the NPV.

50
Q

Relative risk ratio vs odds ratio, difference?

A

Relative risk: out of those exposed, what is the incidence / those not exposed, what is the incidence of disease. Under condition compared to baseline.

Odds ratio, out of those with the disease (like sensitivity), who were exposed to not exposed / out of those without the disease, who were exposed/ not exposed)

Exposed not exposed in both numerator and denominator

51
Q

Relative risk ratio vs odds ratio, difference?

A

Relative risk: out of those exposed, what is the incidence / those not exposed, what is the incidence of disease. Under condition compared to baseline.

Odds ratio, out of those with the disease (like sensitivity), who were exposed to not exposed / out of those without the disease, who were exposed/ not exposed)

Exposed not exposed in both numerator and denominator

52
Q

What are the 4A’s of EBM?

A

Ask (your experimental question), acquire (the information you need) appraise (the value of the information you have gotten), apply (to your subject of interest.

Systematic reviews are already pre-appraised.

53
Q

What is a review article?

What is a systematic review article?

A

It is gathering all the major findings and compiling them into an article.

Strength: if done well, they provide the major findings and focuses as decided by experts in the field

Weaknesses: it is still only one person doing this so it’s subject to bias.

This is what systematic review focuses on, removing the bias. They have a checklist or a criteria set for going about summarizing the best evidence.

54
Q

Describe the steps of a systematic review.

A
  1. have specific question
  2. find all the resources that are relevant (published and unpublished)
    - may limit to RCTs and avoid observational trials.
    - Prevent publication bias (the bias where journals and pharma will only publish articles that have significant outcomes)
  3. sampling method - use exclusion and inclusion criteria for like isolating on RCT to specify the best evidence
  4. Describe the study strength and if quality is associated with the results.
    You will have critical appraisal (review for sources of bias, try to increase internal validity, more than one researcher, weaker studies tend to have larger effect sizes. )
    Forest plot.
  5. Decide whether a metaanalysis can be done.
55
Q

What is Forest plot?

A

Its a comparison of all studies that will show (Sample size and the CI (SD/SEM)) The longer the horizontal line the more variability, the larger the box the more samples.

The shorter and larger, the more weight the study is assigned.

The end total is expressed as a diamond, with the horizontal part of the diamond as the confidence interval

If any of the CI’s contain 1 (if its risk ratios, or is 0) There is no effect and that study or the total is insignificant.

56
Q

DEscribe a meta-analysis

What is a cumulative meta analysis?

A

A quantitative summary - study of studies
Identify studies of interest, compare it to your developed criteria, get your data and do a metaanalysis where you generate summarized estimates.

If results are similar they can be pooled and analyzed together. Requires similar study questions.
Pooled results will be weighted by their sample size

Fixed effect model: studies have asked the same question

Random effect model: assumes there questions are different however they can be related.

Your results will be expressed as total with a forest plot and you can see the significance or lack of.

Cumulative meta analysis - over the years you continuously add more and more data and analyze. As your sample gets larger you will get less variability and a more accurate population mean. Hopefully you’ll see a significance.

57
Q

What is the order of the two pyramids?

A

New pyramid

Individual studies

58
Q

What is the web of causation?

A

Goes against the single cause model

Includes biological, behavioral, and social factor.

Each strand is a possible site for intervention or prevention.

You have to see which risk factors are modifiable (potential for intervention). Hereditary ones are not.

You don’t need to understand all strands to do this.

59
Q

What is the necessary vs sufficient criteria?

A

You can be both or neither.

A cause can be sufficient which means the effect will result even without the presence of other factors

Necessary means without it the effect will not occur and it may need other factors for the result to occur.
ex: HPV and cervical cancer. Without HPV you can’t have cervical cancer.

60
Q

What is the Henle and Koch Single Cause Model: Koch’s Postulates

A

This refers specifically to disease biological agents.

1) The microorganism must be present in all those who are ill and not present in those who are healthy.
Counter: you can carry the organism but not have symptoms

2) The microorganism must be isolated from the diseased organism and grown in pure culture.

3) The cultured microrganism can cause disease when introduced into a healthy host
Counter: disease may require cofactors

4) The same microorganisms can be isolated from the infected host

organism causes disease and can infect and reproduce in a new host.

61
Q

What are the 8 Bradford Hill criteria.

A
  1. Temporality - the only required one
    Exposure must precede disease
    -issues: it may be hard to figure out which came first. Perhaps your factor only made things worse but it had already been initiated by something else? Cross sectional study - two things are correlated but not obvious causation
  2. Strength of the association - odds ratio and relative risk ratio. Strong associations are a good sign of lack of bias and more significant data.
    - issues: strong association doesn’t necessarily mean causation
  3. does response - The disease rate increases with increased exposure.
    - issues: lack of dose response doesn’t mean no causation. You can have a non linear association. It could be threshold where every exposure under it has no effect. U shaped.
  4. Reversibility - removal of the exposure decreases the effects of the disease
    - issues: somethings can’t be reversed, only sometimes. For lung cancer and smoking, you can’t really once the damage is done. OR you have a confounding variable.
  5. consistency - these results are found in different ethnicities, different locations, different individuals etc.
    - issues: if your study is concerned with a subtype there may be less external validity. Or a lack of other information.
  6. Biologically plausible: does this proposed mechanism make sense? Is it possible? consult previous studies.
    - issues: lack of data or a new way of thinking about it.
  7. Specificity: the least important criteria. Exposure is associated with one specific disease outcome. Now we see most diseases have multiple factors and can cause multiple disease.
  8. Analogy: being able to connect it to an already established causal relationship. Ex. inhaling asbestos increases lung cancer.
62
Q

What are ecological studies? aka aggregate studeis.

A

They are weaker studies for concluding causality. They look at the group level rather than the individual level. More like a sociological standpoint. They are used just to notice a possible correlation and then go from there.

In this group, what are they exposed to, what is the outcome?

Ecologic fallacy - we may see an association on a group level but we can’t apply it to the individual level. It’s an average. Don’t ascribe these group results to the individual. These results are only applicable if exposure is homogenous across the group.

It can’t control for confounding variables, it relies on available data which may not be enough, it can’t assess individual data and exposure measures can vary within groups and will bias study findings.

63
Q

What is a pooled analysis and how does it differ from meta analysis?

Common vs different protocol

How does qualitative differ from everything?

A

You are taking all the raw data and compiling them and generating results rather than taking it from the results like meta.
INcreases power and precision

Common protocol - the studies share the same research question

Different protocols - you will likely do a metaanalysis (comparing studies) rather then putting them together and generate messy shit)

Qualitivative reviews are basically what we described for systematic review. You go through the bradford hill criteria and then assign weights to the studies and look for bias.

64
Q

What is primary, secondary and tertiary prevention? ane examples

A

primary is before exposure - sanitation, nutrition, immunization, education

secondary

65
Q

List routine immunizations for adults

Which ones are contraindicated?

A
influenza - annually
tetanus - every 10 years 
varicella - 2 doses 
HPV 3 doses 
MMR 1-2 doses
66
Q

Describe the classic screening principles of disease, population and test.

A

For disease: significant morbidity and mortality, things that kill
-prolonged asymptomatic phase (because if your already showing symptoms wtf is the point of a screen yo)

-effective treatment available, why screen if you can’t do anything afterwards

67
Q

What are three kinds of biases in screening?

A

Lead time- you screen early and the person lives longer with the diagnosis. you caught the disease early on, the person would still live the same amount of time without it

Length-time: you often catch cancers that will not kill and are slow acting, therefore you think your test is really good because you detect something. However you can’t successfully catch the correct ones.
Problem: good at detecting cancers that may never kill and then undergo potentially harmful and costly treatments that may do no good. For the fast ones, you end up diagnosing after symptoms show rather than screening.

Compliance/adherence bias - those who take screening test are more likely to live longer because they are health conscious. Healthy user bias.

68
Q

According to the Number needed to treat, how much do screening tests cost to save one life or improve life expectancy?

A

$50,000

Questions to ask
DOes it even fucking work?
Does it save lives, can it be measured, should it be done…lol

69
Q

Discuss mammography stats.

A

INcidence is quite low, it never reaches 1%.

Over half of the women get false positives.

Same say don’t begin at 40
Start at 50. It doesn’t seem very effective. Actually it is less effective than not. 1 out of 17,000 mammograms needed to correctly identify breast cancer.

Stop screening at age 75. These cancers take a long time to kill and if its longer than your life expectancy, no point.

70
Q

What is the NAM standards for devising trustworthy clinical practice guidelines?

What is the USPSTF (US preventive services)

A

Its about test recommendations, and how to make an evidence based suggestion. Be transparent, avoid conflict of interest, see evidence, look at reviews and keep updated.

THe USPSTF reports yearly gaps in evidence about clinical preventative practices and recommends further examination.

71
Q

WHat is choosing wisely?

A

To encourage conversations that will lead to avoiding unnecessary tests and treatment.
Its based on evidence and tradeoffs, correcting previously held assumptions.

Do not screen for scoliosis, antibiotic treatment on wounds doesn’t decrease your risk for infection. Don’t do many screens lol. Don’t do tests for baseline purposes. Have a significant reason.

72
Q

Research methods: describe abstract, methods, results/discussion.

A

Abstract is a structured way of summarizing the research paper. Different journals have different ways to structure it. JAMA has the largest list of requirements.

Methods are study design (sampling procedures, RCT? sample size, outcomes) and statistical methods. Look at the statistical methods they used (students t test is most common and descriptive statistics)

Results/discussion - findings from the research, depicted as graphs. There will be an interpretation study where they take their results and infer what they think it means. Be wary of how they try to trick you.

73
Q

What are the two types of deficiencies in Abstracts?

A

Inconsistency - data given in abstract is different from that in the body

Omission: data is given in the abstract but not present in the body;

74
Q

What are cross sectional?

A

They measure prevalence. For a moment in time, how many people have this disease or not. It is purely observational so no causation.

75
Q

What is the peer review process?

A

The author submits his paper.
The editor will make a quick decision to keep or reject(not met guidelines, low quality)
The editor hands it to two reviewers, experts and they will be like I think its good
Editor will make the final decision.
Paper published.

76
Q

What are the 2011 OCEBM levels of evidence?

A

Based on your research questions, which kind of article would you look up first.

Systematic is still generally better, The levels don’t really say anything overall about the quality of the evidence. IT will not give you a recommendation.

77
Q

What is critical appraisal?

A

Choose an appropriate tool based on your research question. Answer all the questions in the tool. Come up with your own conclusion based on the tool and be like yeah ill use it or no.

78
Q

Describe the RWJMS general article review sheet.

A
  1. What is the overall design of the study? (RCT, cohort etc)
  2. What is the overall question of the study?
  3. What is the specific question (PICO)
  4. Determine validity (search for biases, inadequate blinding, power)
  5. understand the results and precision of estimates
  6. Can i apply results to the patient? Is the intervention practical, do my patients fall under their category,
  7. Final question, any other issues, how would you summarize the key points.
79
Q

Describe the Oxford Centre Randomised Controlled Trial Appraisal Sheet.

A
  1. What is the specific question (PICO)
  2. where the groups similar at the start of the trial, was there random assignment, were groups treated equally?
  3. Were the measures objective and proper blinding?

What were the results?
-what was the effect size? RR, ARR, NNT
How precise was the estimate: confidence interval.

80
Q

What is consort?

A

Guidelines being followed by most medical journals regarding randomized control trials. 25 item checklist.

81
Q

What is clinical significance and statistical significance?

A

Clinical significance is tighter. You can see statistical significance. If there is no difference statistically and they do not cross the clinical significance line (which is between no difference and difference lines), true negative

If the CI crosses the clinical significance line and the no difference line (inconclusive)

if the CI is significant and does cross the clinical significance line, it is NOT IMPORTANT

if CI crosses the clinical significance line, it is possibly important

If Ci is above the clinical significance line, it is definitely important

82
Q

Systematic reviews exclusion criteria that wouldnt generate bias

A

Excluding types of research designs and excluding types of results. But excluding papers in russian… Lol

83
Q

For people with what conditions is zostavax contraindicated?

A

HIV infection with CD4+ count less than 200

84
Q

what is the order on the new pyramid?

A

Synopses of articles