lecture 7 (slide) Abnormality

8/3/2019 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

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lecture 7 (slide) Abnormality