lecture 7 (slide) Abnormality
Transcript of lecture 7 (slide) Abnormality
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Abnormality
Research Methods
Dent 313
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Normal vs. abnormal
Abnormal means something grossly differentfrom the usual
Distinction between normal & abnormal
Easily identified in obvious cases Needs experience, skills and conceptual basis
when less obvious
Most difficult among unselected patients outside
of hospitals Therefore, calling clinical findings normal or
abnormal is crude and results in somemisclassification
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Normal vs. abnormal
Why to take this crude approach To be perfectly intelligible, one must be
inaccurate, and to be perfectly accurate, onemust be unintelligible Bertrand Russel Physicians usually choose to be intelligible at the
expense of accuracy
Each aspect of clinical work ends in adecision
Pursue evaluation or wait Begin treatment or reassure
present or absent classification is necessary
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Normal vs. abnormal
Examples of obvious abnormal
Missing teeth
Gingivitis
Badly cavitated teeth
Heavily restored teeth
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Normal vs. abnormal
Decision of abnormality can be difficult
Examples:
Appendicitis vs. abdominal pain
Pharyngitis vs. Haemophilusepiglottitis
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Normal vs. abnormal
It is important to distinguish betweenvarious kinds of abnormality
The normal findings require no action
normal vs. within normal limits vs.
unremarkable vs. noncontributory
The abnormal findings are the basis for
action and set out under a problem list Impressions
Diagnoses
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Normal vs. abnormal
Decisions about what is abnormal aremost difficult among none-patients
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Normal vs. abnormal
This lecture will present some of theways clinicians use to distinguishnormal from abnormal by explaining:
how they vary and are distributedamong people
how biologic phenomena are measured
and described how they can be summarized
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CLINICAL MEASUREMENT
Clinical phenomena are measured byscales
Scales are ways of expressing
measurements used for describing clinicalphenomena
Types of scales:
Nominal scale Ordinal scale
Interval scale
Ratio scale
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Giving names to different conditions
Not strictly a scale at all
Cutoff points of normality are defined by
investigator subjectively Examples:
Dramatic discrete events
Death, Dialysis, Surgery, Stroke
Data of two unordered categories (dichotomous)
Present/Absent, Yes/No. Alive/Dead, Sound/Caries
Nominal scale
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Ordinal scale
Listing conditions in some inherent orderor rank of severity without attempting to:
define any mathematical relation between
categories
specify the size of the intervals betweencategories
Cutoff points of normality are defined byinvestigator subjectively
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Ordinal scale
Examples: Ranks:
small, medium, large
Inherent order: mild, moderate, severe
Ordering categories measurable oninterval scale when precision in not
needed E.g., Periodontal pocket depth
Shallow, medium, deep pockets
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Interval scale
Also called numericalor dimensional
Listing conditions in inherent order
The numbers used in the measuring scale have a
mathematical relation to one another Intervals between successive values are equal
The scale has no true zero value and -ve valuescan exist
E.g., Temperatures F
or C
Cutoff points of normality can be decided
precisely
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Ratio scale
The same as interval scale but has a truezero value
-ve values do not exist
Cutoff points of normality can be decidedprecisely
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Two types of Ratio scale
Continuous scale
Can take any value in a continuum
E.g., wt, bp
May take integer values for rounding
Discrete scale
Specific values expressed as counts
E.g., # of pregnancies, # of births with cleft lip-palate, # of missing teeth
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Performance of measurements
Validity
Reliability
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Validity
The degree to which the data measurewhat they were intended to measure
Validity = accuracy
Repeated validity checks
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Reliability
The extent to which repeatedmeasurement of a stable phenomenonby different people and instruments at
different times and places get similarresults
Reliability = reproducibility = precision
Established by repeatedmeasurements
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Validity vs. reliability
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Variation
The range of values that a clinicalmeasurement of the samephenomenon can take
Overall variation
The sum of
Variation due to the act of measurement
Variation due to biologic differences within individuals from time to time
among individuals
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Variation due to measurement
Role of validity and reliability
Lack of validity biased results(systematic error)
Lack of reliability random error
Objective machine measurement vs.subjective human judgment
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Measurement vs. biologic variation
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Distribution
Data measured on interval scales can bepresented as a frequency distribution
Central tendency middle of distribution
Dispersion how spread out the value are Unimodal distribution one hump
Skewed distribution
Clinical distribution vs. normal distribution
Not identical although clinical distribution isassumed normal for convenience
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Normal distribution
Gaussian distribution
Symmetrical bell shaped
Dispersion is the same on both ends
Dispersion is only due to randomvariation
68.26% fall within 1 SD
95.44% fall within 2 SDs 99.72% fall within 3 SDs
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Normal distribution
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Hard and Soft measurements
Hard measurement
Usually applied to data that are reliableand preferably dimensional
E.g., laboratory data, demographic data,and financial costs.
Soft measurement
E.g., clinical performance, convenience,anticipation, and familial data
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Criteria for abnormality
Distinction between normal andabnormal is hard: Sometimes normal and abnormal are not
distinct in population there is a smooth transition from low to
high values of dysfunction withoverlapping degrees for disease and
normal Disease is acquired by degrees (mild vs.
severe)
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Abnormal as Unusual
Normal = most frequently occurring=usual One commonly used way that all values
beyond 2 SD from the mean are abnormal
Beyond the 95
th
percentile
X
+1SD
+2SD
-1SD
-2SD
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Abnormal as Unusual
Situations that unusual is misleading Frequency of abnormal among different
diseases Not necessarily beyond 95th percentile is abnormal in
all diseases Example: WHO blood Hb
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Cholesterol level
82
252
286
%
Increase risk from 82 to 286Cases / 1000/24 yr
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Abnormal as Unusual
Situations that unusual is misleading
Some extreme unusual ones readings arepreferable to more usual ones
E.g., low blood pressure
Statistically normal and clinically diseased
Normal pressure of glaucoma
Ab l i t d ith
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Abnormal as associated withdisease
Abnormal are those observationsregularly associated with disease,disability, or death
Abnormal = any clinically departurefrom good health
Example: 95.2% of population have uric
acid 7mg/100 ml and impossible todevelop gouty arthritis
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Abnormal as Treatable
Considered abnormal when thetreatment leads to a better outcome
If removal of risk factor does not
remove risk it is not necessary to labelpeople abnormal
What is considered treatable changes
with time E.g., folic acid level to prevent anemia