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Foundations 1 > 1-CM > Flashcards

1-CM Flashcards

1
Q

3 principles of EBM

A
  1. Clinical decisions require systematic summaries of the best available evidence
    1. EBM provides guidance to distinguish between high and low quality evidence
    2. Clinical decisions require explicitly balancing risks benefits and patient values and preferences
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2
Q

3 questions of EBM/MDM

A
  1. How serious is the risk of bias?
    1. What are the results?
      Can I apply this to my patient?
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3
Q

Innumeracy

A

Inability to understand numbers/statistics

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4
Q

Quantitative decision making

A

Answering (clinical) questions by testing well-specified hypotheses through precise measurement and quantification of predetermined variables that yield numbers (or results) suitable for statistical analysis, includes cost-effectiveness analysis

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5
Q

Evidence based medicine

A

“modern term for application of clinical epidemiology to the care of patients” Conscientious, explicit, judicious use of current best evidence in making decisions about the care of individual patients, Integrate individual clinical expertise with best available external clinical evidence, Integration of best research evidence with clinical expertise and patient values

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6
Q

Clinical epidemiology

A

Science of making predictions about individual pts by counting clinical events (the 5Ds) in groups of similar patients and using strong scientific methods to ensure that the predictions are accurate. Purpose: to develop and apply methods of clinical observation that will lead to valid conclusions by avoiding being misled by error/chance.

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7
Q

Bias

A

“a process at any stage of inference tending to produce results that depart systematically from the true values” “an error in the conception and design of a study or in the collection/analysis/interpretation/publication/review of data leading to results of conclusions that are systematically (as opposed to randomly) different from the truth”

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8
Q

Selection bias

A

Selection bias: occurs when comparisons are made between groups of patients that differ in determinates of outcome other than the one under study, issue when patients chosen for a study

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9
Q

Measurement bias

A

Measurement bias: occurs when the methods of measurement are dissimilar among groups of patients, method of measurement leads to systematically incorrect results, ex white coat hypertension

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10
Q

confounding bias

A

Confounding: occurs when 2 factors are associated (travel together) and the effect of one is confused with or distorted by the effect of the other, ex. People who take antioxidants have lower rates of heart disease…, issue when analyzing data of a study

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11
Q

Chance

A

a given sample even if selected without bias may misrepresent the situation in the population as a whole because of chance, divergence of an observation on a sample from the true population value due to chance alone is called random variation, cannot be eliminated from studies but statistics can help account for

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12
Q

Internal validity

A

degree to which the results of a study are correct for the sample of patients being studied- applies to the conditions of the particular group of patients being observed and not necessarily to others, for a study to be useful is a necessary but NOT sufficient condition

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13
Q

External validity

A

degree to which the results of an observation hold true in other settings, generalizability, expresses the validity of assuming that patients in a study are similar to other patients

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14
Q

Prevalence

A

fraction of a group of people possessing a clinical conditions or outcome at a given point in time. Survey defined population and count number with / without condition of interest

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15
Q

Incidence

A

fraction or proportion of a groups of people initially free of the outcome of interest that develops the condition over a giver period of time. Refers to NEW cases occuring in a population initially free of disease

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16
Q

Random sample

A

every individual in the pop has an equal probability of being selected

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17
Q

Probability sample

A

every person has a know (not equal) probability of being selected, helps include a specified number of elderly or ethnic minorities

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18
Q

Convenience samples

A

folks who visit the clinic/easy to gather

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19
Q

Grab samples

A

clinicians just grab patients wherever they could find them

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20
Q

Statistical significance

A

p-alpha < 0.05, arbitrary

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21
Q

Hypothesis testing

A

asks whether an effect is present or is not by using stats test to examine hypothesis that there is no difference. Can says either 1. effect is present or 2. insufficient evidence to conclude an effect is present

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22
Q

Null

A

there is no difference between two groups

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23
Q

Type 1 (alpha) error

A

“false positive”, probability of saying there is a difference in treatment effects when there is not, often set at 5%

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24
Q

Type 2 (beta) error

A

“false negative”, probability of saying there is no difference in treatment effects when there is, often set at 20%

25
Q

Inferential statistics

A

estimates the probability of error due to random variation and is basis for calculation of the probability that the results could have occurred by chance alone

26
Q

p-value

A

associated with hypothesis testing, quantitate estimate of the probability that differences in treatment effects in the study could have happened by chance alone assuming that there is in fact no difference between the groups, ex: “if there were no difference between treatment effects and the trial was repeated many times, what proportion of the trials would conclude that the difference between the two treatments was at least as large as that found in the study?” (Palpha) (Pbeta: error resulting from random variation type 2 errors)

27
Q

confidence interval

A

if the study is unbiased, there is a 95% chance that the interval includes the true effect size- the more narrow the CI the more certain one can be about the size of true effect, advantage over p value: put the emphasis where it belongs (on the size of the effect) helps show range of plausible values and decide whether an effect size they regard as clinically meaningful is consistent with the data, also give info on power

28
Q

power

A

probability that a study will find a statistically significant difference when a difference really exists is call ed power, sensitivity of a diagnostic test

29
Q

Sample

A

size of sample needed for power depends on: 1. magnitude of the difference in outcome between treatment groups, 2. Palpha 3. Pbeta, 4. underlying outcome rate

30
Q

precision

A

expressed as a CI, around the point estimate (the effect size that is observed in a study is called the point estimate of the effect, best estimate from the study of the true effect size and is the summary stat usually given the most emphasis in research reports), increases with the power of a study

31
Q

accuracy

A

the degree to which the result of a measurement, calculation, or specification conforms to the correct value or a standard

32
Q

Non-parametric

A

stats that do not assume a normal variation of the population being studied

33
Q

Two-tailed

A

either more OR less effective than treatment being compared to

34
Q

Randomized controlled trial

A

A study in which people are allocated at random (by chance alone) to receive one of several clinical interventions. One of these interventions is the standard of comparison or control. The control may be a standard practice, a placebo (“sugar pill”), or no intervention at all.

35
Q

Inclusion criteria

A

Inclusion criteria are characteristics that the prospective subjects must have if they are to be included in the study

36
Q

exclusion criteria

A

characteristics that disqualify prospective subjects from inclusion in the study

37
Q

randomization

A

A method based on chance alone by which study participants are assigned to a treatment group. Randomization minimizes the differences among groups by equally distributing people with particular characteristics among all the trial arms. The researchers do not know which treatment is better

38
Q

allocation concealment

A

Allocation concealment is a different concept to blinding. It means that the person randomising the patient does not know what the next treatment allocation will be. It is important as it prevents selection bias affecting which patients are given which treatment (the bias randomisation is designed to avoid).

39
Q

blinding

A

a procedure in which one or more parties in a trial are kept unaware of which treatment arms participants have been assigned to, in other words, which treatment was received. Blinding is an important aspect of any trial done in order to avoid and prevent conscious or unconscious bias in the design and execution of a clinical trial

40
Q

intervention

A

subjects assigned to intervention at randomization

41
Q

control

A

no intervention arm of trial

42
Q

placebo effect

A

a beneficial effect produced by a placebo drug or treatment, which cannot be attributed to the properties of the placebo itself, and must therefore be due to the patient’s belief in that treatment.

43
Q

intention to treat analysis

A

a technique used in randomized controlled trials (RCTs), where patients are compared–in terms of their final results–within the groups to which they were initially randomized, independently of receiving the allocated treatment, having dropped out of the study or having violated the initial protocol (for whatever reason). In other words, it constitutes an analysis of the results based on the treatment arm to which the patients belong due to the initial random allocation, and not on the treatment actually received (active or placebo). Intention-to-treat analysis permits the pragmatic evaluation of the benefit of a treatment change, and not the potential benefit in patients getting the pre-planned allocated treatment only.

In noninferiority trials, both intention to treat and per-protocol analysis are recommended; both approaches should support noninferiority.

44
Q

per-protocol analysis

A

Per-protocol analysis is a comparison of treatment groups that includes only those patients who completed the treatment originally allocated. If done alone, this analysis leads to bias.

In noninferiority trials, both intention to treat and per-protocol analysis are recommended; both approaches should support noninferiority.

45
Q

loss-to-follow up

A

subject loss to follow up can introduce bias

46
Q

cross-over

A

a repeated measurements design such that each experimental unit (patient) receives different treatments during the different time periods, i.e., the patients cross over from one treatment to another during the course of the trial. This is in contrast to a parallel design in which patients are randomized to a treatment and remain on that treatment throughout the duration of the trial.

The reason to consider a crossover design when planning a clinical trial is that it could yield a more efficient comparison of treatments than a parallel design, i.e., fewer patients might be required in the crossover design in order to attain the same level of statistical power or precision as a parallel design

47
Q

efficacy

A

Intervention studies can be placed on a continuum, with a progression from efficacy trials to effectiveness trials. Efficacy can be defined as the performance of an intervention under ideal and controlled circumstances, whereas effectiveness refers to its performance under ‘real-world’ conditions.

48
Q

effectiveness

A

pragmatic trials

Intervention studies can be placed on a continuum, with a progression from efficacy trials to effectiveness trials. Efficacy can be defined as the performance of an intervention under ideal and controlled circumstances, whereas effectiveness refers to its performance under ‘real-world’ conditions.

49
Q

Relative risk (RR)

A

a ratio of the probability of an event occurring in the exposed group versus the probability of the event occurring in the non-exposed group

When a treatment has an RR greater than 1, the risk of a bad outcome is increased by the treatment; when the RR is less than 1, the risk of a bad outcome is decreased, meaning that the treatment is likely to do good.

The relative risk (RR) of a bad outcome in a group given intervention is a proportional measure estimating the size of the effect of a treatment compared with other interventions or no treatment at all. It is the proportion of bad outcomes in the intervention group divided by the proportion of bad outcomes in the control group. In this hypothetical case, the RR is 0.6 (12 per cent ÷ 20 per cent = 0.6)

50
Q

Absolute risk reduction (ARR) / Risk difference

A

is the most useful way of presenting research results to help your decision-making:

For instance, supposing that a well-designed randomised controlled trial in children with a particular disease found that 20 per cent of the control group developed bad outcomes, compared with only 12 per cent of those receiving treatment. In this example, the ARR is 8 per cent (20 per cent - 12 per cent = 8 per cent).

51
Q

Number needed to treat (NNT)

A

In this example, the ARR is 8 per cent (20 per cent - 12 per cent = 8 per cent). This means that, if 100 children were treated, 8 would be prevented from developing bad outcomes. Another way of expressing this is the number needed to treat (NNT). If 8 children out of 100 benefit from treatment, the NNT for one child to benefit is about 13 (100 ÷ 8 = 12.5).

52
Q

Number needed to harm (NNH)

A

epidemiological measure that indicates how many persons on average need to be exposed to a risk factor over a specific period to cause harm in an average of one person who would not otherwise have been harmed.

calculated by dividing 1 by the absolute risk increase, and again multiplying by 100 when the ARI is expressed as a percentage.

53
Q

efficacy vs effectiveness

A 
Efficacy
How an intervention would perform under ideal circumstances
Patients follow all instructions
No comorbid disease
Best possible care

Effectiveness
What would happen in real life
Sometimes patients don’t follow the rules
A treatment plan may not be followed perfectly

Difference between the two is the “implementation gap”- gap between ideal care and ordinary care

54
Q

Pre-pre analytical

A

Pre-pre analytical
Role: Utilizing medical decision-making capacity to order the correct test
Potential errors: (1) Failing to order a necessary test, (2) ordering the wrong test, (3) ordering unnecessary tests (i.e. not evidence-based)

55
Q

Pre-analytical

A

Pre-analytical – has the highest risk for error due to having many people/processes involved!!!
Role: Getting the correct sample from the correct patient
Potential errors: Unlabeled or mislabeled specimen could lead to (1) wrong test on wrong patient, (2) wrong result in the patient chart, (3) wrong diagnosis, and (4) delay in treatment due to bad result

56
Q

Analytical

A

Analytical
Role: Getting the right result, this is the labwork portion of the testing
Potential errors: Obtaining false results from random or systematic failure of laboratory processing

57
Q

Post-analytical

A

Post-analytical
Role: Reviewing, verifying, and interpreting correct results by laboratory staff
Potential errors: (1) Delays in treatment due to untimely test review, (2) incorrect results (human error) through result entry error or verification errors

58
Q

Post-post analytical

A

Post-post analytical
Role: Clinical interpretation of results by the clinical care team
Potential errors: Mistreatment due to gaps in knowledge about result interpretation

59
Q

components of social history

A 
Living situation
Daily life and activities
Support systems
Presence of caregiver, etc.
Race and ethnicity
Values
Stresses 
Sexual history

Foundations 1 (41 decks)

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