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Flashcards in Intro Deck (23):
1

Twomey

Clinical decision making: combination of implicit and learned characteristics

2

Wainwright

directive vs informative factors

3

Sackett/Guyatt

Evidenced based practice, vs evidence informed practice

4

EBP

Deduction: CDM based on studies designed to answer clinical questions, but diminishes clinical experience, patient values, anecdotal/empirical evidence

5

EIP

induction, abdication, deduction, clustering

6

Sackett: 3 legged stool

clinical experience, research, patient characteristics

7

Pathways of thinking

Sackett

8

Algorithms

step by step protocol, arboization: A+B=C

9

Exhaustion

gathering all patient info before sifting through

10

Hypothetico-deductive reasoning

forming a short lift of potential diagnoses and then performing tests to rule in or out based on subjective

11

Pattern recognition

recognizing condition from past patient scenarios: quick, but can be wrong

12

Sense making and small wins

breaking down complex problems into smaller parts to address individually

13

Occam's Razor

the simpler explanation, all things equal, is better than a more complex one.

14

Cognitive Biases: clinical delusional

overconfident in your judgements

15

Cognitive biases: sunk-cost effect

following a course of action after a lot of investment in it.

16

Cognitive biases: recency effect

over emphasis on info most readily available to use of what we've seen most often lately

17

Cognitive biases: confirmation bias

gathering and believing in info that confirms or existing views and not considering what challenges those views (most common)

18

Cognitive biases: anchoring bias

allowing an initial reference point to distort our estimations

19

Cognitive biases: illusory correlation

drawing a relationship between 2 variables that doesn't exist

20

cognitive biases: hindsight bias

looking at past outcomes as easily predictable when they were not easy to foresee. over crediting ourselves with an outcome

21

Prognosis

Qualitative (options), Quantitative (probability), temporal (time)

22

Likelihood ratio

tells us how the odds of having a disorder change with either a positive or negative test. Mathematically combines sensitivity and specificity of a test. Spin and Snout

23

Fagan-Baye's Theorem

Describes the probability of an event based on the conditions that might be related to that event - why clustering is important.