# 1-CM Flashcards

3 principles of EBM

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

3 questions of EBM/MDM

- How serious is the risk of bias?
- What are the results?

Can I apply this to my patient?

- What are the results?

Innumeracy

Inability to understand numbers/statistics

Quantitative decision making

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

Evidence based medicine

“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

Clinical epidemiology

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.

Bias

“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”

Selection bias

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

Measurement bias

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

confounding bias

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

Chance

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

Internal validity

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

External validity

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

Prevalence

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

Incidence

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

Random sample

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

Probability sample

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

Convenience samples

folks who visit the clinic/easy to gather

Grab samples

clinicians just grab patients wherever they could find them

Statistical significance

p-alpha < 0.05, arbitrary

Hypothesis testing

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

Null

there is no difference between two groups

Type 1 (alpha) error

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