(6) Abnormalities

7/30/2019 (6) Abnormalities

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ABNORMALITIESS

Slide 2-8: Normal vs. abnormal

Abnormal means something grosslydifferent from the usual

And here I put the grossly in red, grossly means significant in change, so in order to

be abnormal it has to be different from the usual, the thing that you are used to, andthis difference has to be significant, not just a small difference.

If I see a person who is slightly tired I can't say that this person is abnormal,

he is just slightly tired so he is slightly different from the usual, but if he is

very sick, he can't stand and he is all the time staying in his bed, this is

abnormal, so he is grossly or significantly different from the usual.

Distinction between normal & abnormal

Easily identified in obvious casesIn Down syndrome it is easy to tell that this person is abnormal and has a disease.

Needs experience, skills and conceptual basis when less obviousHere we need experience to be able to distinguish between normal and abnormal.

And in your role as a future dentist B-) you dont have to treat everything in the

mouth, because some conditions needs specialist.

During filling a molar in a patients mouth, you noticed squamus cell carcinoma, at

the posterior third of the tongue, you don't have to treat it but at least you

have to identify the abnormal condition and then refer the patient to a

specialist, so as a physician or as a dentist you don't need to treat everything

but you have to identify normal from abnormal.


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Most difficult among unselected patients outside of hospitalsSometimes if you see a patient outside the hospital it's very difficult to identify that

he has an abnormality, but in the hospital it is easier, because you can have the

investigation done for the patient to be able to tell whether he is normal or

abnormal. Therefore, calling clinical findings normal or abnormal is crude and results in

some misclassification

So sometimes it is not easy to distinguish between normal and abnormal, it may not

be accurate and result in some miss classification.

Why to take this crude approach

We know that sometimes in certain cases to distinguish between normal and

abnormal is crude, or not accurate, why do we need to take this inaccurate or crude

approach?

To be perfectly intelligible, one must be inaccurate, and to be perfectly accurate,

one must be unintelligible

Bertrand Russel

- Physicians usually choose to be intelligible at the expense of accuracy, so wehave to be intelligible more than being accurate

Each aspect of clinical work ends in a decision

Pursue evaluation or wait Begin treatment or reassure

o present or absent classification is necessary

A patient come to you suffering from something painful, you examine the patient

and you found nothing, you just reassure, you dont have to treat the patient, so

either you continue the evaluation or you wait to begin the treatment or

sometimes we just assure the patient and ask the patient to leave without any

treatment.


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Examples of obvious abnormal

Missing teeth Gingivitis Badly cavitated (caries) teeth heavily restored teeth

Decision of abnormality can be difficult, less obvious.

a very small cavity at the mesial or distal surface, may need x-ray distinguishing between Appendicitis and abdominal pain Pharyngitis vs. Haemophilus epiglottitis

It is important to distinguish between various kinds of abnormality

The normal findings require no actiono normal vs. within normal limits vs. unremarkable vs.

noncontributory

In some cases the word normal is not a single point, we have to say within normal

limits, this means that normal is a range, there is an lower limit and upper limit,

within this range we say this is normal, that is why we dont say in practice that this

patient is normal, or the oral features of this patient are normal, we say it is withinnormal limits.

some people have clicking in the temporomandibula joint and am sure at least ten

of you have clicking here, this is not something 100% normal but it is still

abnormal, as long as it is not painful, so for example this patient has clicking in

the TMJ but it's not painful so he is within normal limits, so it's not a single

point it is rather a range.

Sometimes we can say unremarkable or noncontributory these are different terms

to describe normal, but we dont usually use normal unless you can say that normal

is a single point

In the presence of caries, I can't say that this cries is within normal limit, the

presence of carries indicates for abnormality without range.


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The abnormal findings are the basis for action and set out under a problemlist

When you find something abnormal, you will have first off all some impression

about the level of abnormality about this condition, and this finally leads to

diagnosing this problem

Decisions about what is abnormal are most difficult among none-patients

As I said, when you examining someone outside the hospital it is difficult to say if this

person is normal or abnormal, because the lack of the setting to take investigations,

like blood tests.

This lecture will present some of the ways clinicians use to distinguish normal from

abnormal by explaining:

how they vary and are distributed among people how biologic phenomena are measured and described how they can be summarized

Slide 9: CLINICAL MEASUREMENT

Clinical phenomena are measured by scales (mawazeen)

Scales are ways of expressing measurements used for describing clinicalphenomena

Types of scales:

Nominal scale Ordinal scale Interval scale Ratio scale Slide (10, 11, 12, 13, 14.15)

Nominal scale

Giving names to different conditions

Not strictly a scale at all, you are just giving namesIs the patient dead, or is he having dialysis, surgery, or stroke? sometimes you

describe things using words, also present or absent, yes or no,alive or dead, these

are examples of nominal skills, you are not measuring or giving numbers, but you are

examining a patient, like if this tooth is carious or not, this tooth is present or

missing, you are giving description or names to describe things, this is called nominalscale, the other term for nominal scale is categorical scale.


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Cutoff points of normality are defined by investigator subjectively

Because we are not using numbers, we are using words to describe, so that it is why

it varies from an investigator to another, it is subjective and not objective.

We always try to describe things in quantitative measures but some cases can't be

described quantitatively, so we use the nominal scale.

Ordinal scale

Here we give words too, but in this scale we have some order and ranking

Listing conditions in some inherent order or rank of severity withoutattempting to:

Define any mathematical relation between categoriesWe cannot say that the word 'medium' is three times bigger than the word

'small'!

Specify the size of the intervals between categoriesWe cannot say that the difference between small and medium is the same as

the difference between medium and large!

Cutoff points of normality are defined by investigator subjectively

-Ranks:

small, medium, largeYou are examining the body building of a patient, you are saying this

patient is small medium or large, in this case you are giving words to

describe, but these words have some orders.

-Inherent order:

mild, moderate, severeA patient suffers from a pain; it can be described as mild moderate or severe.

-Ordering categories measurable on interval scale when precision in not needed

Periodontal pocket depth: Shallow, medium, deep pockets


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Interval scale

Also callednumericalordimensional, because we use numbers in this scale

Listing conditions in inherent order, so it is similar to the ordinal scale in having an

order, but instead of having words like mild moderate and severe we have numbers.

The numbers used in the measuring scale have a mathematical relation toone another.

Intervals between successive values are equal.We are measuring the temperature, when we say today it is twenty Co and

two month ago it was forty Co, we can say that forty is double twenty

(40=20*2) so there can be mathematical relationship between them. And

there are equal distances between measurements, so that the difference

between 10 and 20 is the same as 20 and 30.

The scale has no true zero value and -ve values can existWhen we say that the temperature is 0 Co, does it mean that there is no

heat at all!!! Can't it become any colder! NO, so the Celsius zero is not a

true zero, the absence of heat is measured by another method called

Kelvanic at (-273Co), so the actual zero is the 0 Ko where there is no heat

at all, but the 0Co is not a true zero, so Kelvanic temperature is not an

interval scale, it is a Ratio scale, because it has a true zero.

Cutoff points of normality can be decided preciselyWe can specify a point of normality and abnormality, for example in zero

Co water begins to solidify to become ice.

Ratio scale

The same as interval scale but has a true zero value

-ve values do not exist, we cannot say this person is -1.50 M tall!!! Cutoff points of normality can be decided precisely

I want to measure the weight of a person, a person who is as tall as me

1.80M should be let's say 80Kg but if my weight were one 100Kg, I say

this is abnormal, so we also can specify a point of normality and

abnormality in ratio scale.


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Two types of Ratio scale

Continuous scale-Can take any value in a continuum, it accepts decimal points and

integers.

-Weight, Blood pressure, Height

-May take integer values for rounding, (ta7weel 2la aqrab 3adad sa7ee7)

176.3 CM 176 CM

Discrete scale-Specific values expressed as counts, doesnt accept decimal points, only

integers.

The number of pregnancies, births with cleft l ip-palate, missing teeth, we cannot say

that this person has 2.5 teeth messing!

Slide (16, 17, 18, 19)

Performance of measurements

Validity

Reliability

Validity

The degree to which the data measure what they were intended to measure, in the

first lecture we specified this.

Validity = accuracy

Repeated validity checks, we do it more than once to be more accurate.

Reliability

The extent to which repeated measurement of a stable phenomenon by different

people and instruments at different times and places get similar results.

For example I want to measure the length of Maher (ajmal ta7eyyat lel Gentle man

Dr.Maher ) let's assume he is 1.75M, another person measured Maher too but with a

different instrument, if the result is the same, we can say that it is reliable, but

accuracy means that 1.75M is the actual length of Maher.


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Reliability = reproducibility = precision

Established by repeated measurements

Validity vs. reliability

Validity high (A&C): The distributions of the results are around the true value (accurate). Validity low (B&D): the distribution of the results are shifted from the true value. Reliability high (A&B): The distributions of the results are close from each other (precision). Reliability low (C&D): The distributions of the results are far from each other.

Slide 20, 21

Variation

The range of values that a clinical measurement of the same phenomenon can take

Overall variation

The sum of-Variation due to the act of measurement

We can get different measurements or results, when two different persons

examine the same patient, or duo to using two different methods, or measured

in two different times.

The actual measurement

(True value)


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-Variation due to biologic differences:

Within individuals from time to time Among individuals

When we examine the length of all the students here, we will get variation in the

results, because we have long students, medium and short students, so this

variation is duo to biological differences.

Variation due to measurement

Role of validity and reliability

Lack of validitybiased results (systematic error) Lack of reliabilityrandom error

When we use different instruments or different people to measure something, we

can get variation in the results, which is an error, and this error is called ' random

error' and it is acceptable in the research to some extent, but if you have lack of

validity, it is called systematic error which should be avoided in the research.

Random error: In the research that I did about the timing of the eruption of

the permanent teeth, I examined some patients and I had three assistants toexamine patients too, of course my measurement can be different from there

measurement but this differences is called inter examiner error, it is still a

random error, it is not an intended error so it is not bias.

Systemic error: if I measure a person and get the result of 1.70M and in fact

he is 1.60M we have difference in height, so this is lack of validity, and it is not

a random error, it is a systemic error.

Objective machine measurement vs. subjective human judgment

When we use the subjective human judgment we can get random errors, but we can

be more precise in using objective machine measurements, so the amount of error

becomes less and less.


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Slide 22: Systematic error vs. random error

Systematic error

Biases pushing the values of separate measurements away from the truevalue

Remains systematically different no matter how many times themeasurement is repeated

Random error

Even distribution about the true value Various biases tend to balance each other out

If I have a systematical error I will get measurements which are not actually true, no

matter how many times I repeat it, but in the random error, they are close to the

true value.

When the actual length for the person is 1.70 I can get by certain instrument

1.72M, so 2 cm over, and another instrument is 1.68M, so when you do it again

and again u can have an upper values and lower values, and these values can

cancel each other out, so in random error we have even distribution about the

true value.

that is why random error is acceptable in some limits, because In random error I

have higher and lower measurements, and if I do many many measurements the

amount of high measurement will be similar to the amount of low measurement,

and they cancel each other out and by this the random error becomes very small.

Measurement vs. biologic variation


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In the sketch above:

First condition: the result of examining a certain patient by one observer, at one specific point of time

Second condition: the result of examining a patient by two observers simultaneously at the same time.

Third condition: the result of examining a patient by one observer at different points of time.

Fourth condition: the result of examining a group of people at a specific point of time .

The distribution increases from the first condition to the last condition.

Slide 25, 26, 27: Distribution

Data measured on interval scales can be presented as a frequency distribution

Central tendencymiddle of distribution. (Point zero)

Most of the measurements tend to accumulate in the center of the distribution.

Dispersion how spread out the value are

Unimodal distribution one hump

Skewed distribution, when the hump is shifted to the right or left.

Clinical distribution vs. normal distribution

Not identical although clinical distribution is assumed normal forconvenience

Normal distribution

Gaussian distribution

Symmetricalbell shaped

Dispersion is the same on both ends

Dispersion is only due to random

variation

Standard deviations:

68.26% fall within 1 SD (+1 to -1)

95.44% fall within 2 SDs (+2 to -2)

99.72% fall within 3 SDs (+3 to -3)


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When I made an examination on the results of your exams, I have to follow the

normal distribution, people scoring 70 will be at the zero point because we have

many many peoples scored 70, but people who are very smart they are fewpeople, we have only ten people who scored 90 and over, and we have 20 people

who failed, so that is why the good exam has to follow the normal distribution.

In oral histology curse last year, people who scored in the eighties they only got

one mark bonus but people who got 47 they got five marks bonus, I made that to

make the distribution normal, because the hump was not in the center, it was

deviated to the left side, which is called skewed. (allah ye5allelna 2l skewed :P )

Slide 27:Hard and Soft measurements

Hard measurement

Usually applied to data that are reliable and preferablydimensionalWe said reliable data means, if I do the measurement more than once, the result of

the second measurement should be similar to the first one.

I measured the height of a person and it was 1.70M, if I measured it again

tomorrow and after one week, it should be around 1.70. But if I measured hisheight after one week and I found that its 1.8, then my measurement is not

reliable. So, hard measurement should be precise

E.g., laboratory data, demographic data, and financial costs.Soft measurement

E.g., clinical performance, convenience, anticipation, and familial dataOn the other hand, soft measurement can be less precise or not very accurate. So, I

can divide my measurements to hard and soft measurement

If I want to measure the height of a person precisely, it is as we called hard

measurement, and this is usually represented by dimensional, which means

interval or ratio scales (in terms of numbers). Now, if I want to say, this person

is tall, medium, or short, this is not actual quantitative (not number) we call it

categorical, and this is the soft measurement.


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Another example for soft measurement, if I want to measure the clinical

performance, like if I want to describe Maher as a good dentist (a perfect one

actually ;) ), average, or poor dentist, am not using number for that and its not

very precise, its just a description.

Also convenience is an example of soft measurement, if you are happy about

your dental treatment as a patient. Anticipation (expectation) and familial data

are also related to soft measurements.

So, this is the difference between hard and soft measurements. Generally speaking,

hard measurements are usually measured by interval or ratio scales, but soft

measurements are measured with ordinal or categorical scales.

Slide (28, 29): Criteria for abnormality

Distinction between normal and abnormal is hard:

Sometimes normal and abnormal are not distinct in populationDistinguishing between normal and abnormal can be hard and other times its very

easy.

If you are walking on the street and you saw a person with Down syndrome, you

can easily say that this person is abnormal. Anything related to genetics can beeasily identified as abnormal. But sometimes you cannot say whether someone is

well or not well, you can see a person who is very well but he might be suffering

from something and he is hiding it. Another thing, outside the hospital, its

difficult to distinguish between normal and abnormal because you cant measure

that, but inside the hospital its easy because you can measure that and say if

the person is in pain or not.

There is a smooth transition from low to high values of dysfunction withoverlapping degrees for disease and normal

High lipid (cholesterol) in the blood, theres no point to say people below this

point or level of cholesterol are healthy and people above that are abnormal. So

theres an overlap between normal and abnormal between cases. Disease is acquired by degrees (mild vs. severe)

A pain starts mild and this becomes moderate and after that severe and for

that its difficult to tell that mild pain is here and severe pain is there.


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Since there is NO sharp dividing line between normal and abnormal

The clinician can choose when to consider it disease Based on what??????

In certain diseases where its difficult to say where the point is between normal and

abnormal, its up to the clinician to decide, and for sure it varies from one clinician to

another; and that must be based on certain criteria that the clinician chooses.

three criteria have proven usefulUsually we have 3 criteria that proven useful. Sometimes in situations where its

difficult to distinguish between normal and abnormal, there are some criteria that

can be helpful making this easier.

Slide (30-35)

FIRST CRITERIA: Abnormal as (means) Unusual

Normal = most frequently occurring=usual

This criterion says, anything unusual is

considered abnormal; but this is not precise

all the time. Normal means most frequently

occurring, which is usual.

Most of us now are healthy but some of us

might be sick at this moment, but the

majority of us are normal, Can we say that normal people are frequent people and

sick people are the exception? No, Not necessarily all the time.

One commonly used way that all values beyond 2 SD from the mean are abnormal

This criterion says that anything above (+2) standard deviation (SD) or below (-2) SD

is considered as abnormal because its unusual. We can be successful with this

criterion in some situations, but sometimes we aren't.

Beyond the 95th

percentile

It can be also be represented by percentile. (+2 SD means 97.5 percentile and -2 SD

means 2.5 percentile) So, you either depend on the SD or by the normal distribution

by percentile; anything beyond 95 percentile is considered as abnormal in this

criterion.


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Situations that unusual is misleading

So we have situations where this criterion is misleading or is difficult, examples:

Frequency of abnormal among different diseases-Not necessarily beyond 95

thpercentile is abnormal in all diseases

Some diseases are extremely rare. If we have a disease that occurs in 1 case

per 1 million in population, its not actually pretending 95 percentile 1 part per

melon represent 99.999.. percentile. And its also not accurate in very common

diseases like caries because more than 5% of the population has caries. So, 95%

as a point of abnormality is not accurate for rare and very common diseases.

Another example of a very common disease is hemoglobin (Hb); people say if Hbis less than 12, it is anemia. But actually people who are below 12 are not 5% as

this criterion indicate, they are 12%.

There is a risk of disease from low normal to high normal with no cutoff pointdividing normal from increases risk

The relationship between Cholesterol and the congestive heart disease (CHD).

Can you say that any person with abnormal cholesterol level is having CHD? NO,

many people even in this class (:S!) may have cholesterol above the normal limitbut they dont have CHD so here is no cut point between normal and abnormal, it

is a gradual transition from normal to abnormal.

Some extreme unusual ones readings are preferable to more usual onesLow blood pressure, its sometimes more dangerous to the heart than high blood

pressure. So, we cant say that people with high blood pressure are more

susceptible for the disease, because we also have people with low blood pressure

who are more susceptible for the disease.

Statistically normal and clinically diseasedLike in normal pressure of glaucoma, which is increased pressure in the eye,

some people have glaucoma but when we measure the pressure in the eye, we

found it normal but they are actually diseased.


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Second criteria: Abnormal as (means) associated with disease

Abnormal are those observations regularly associated with disease, disability, or

death

Abnormal = any clinically departure from good health

95.2% of population has uric acid above the limit, which is 7mg/100 ml and these

are impossible to develop gout. We know that when uric acid increases, it

develops gout. But many people have increased level of uric acid but they still

dont have gout,but we cant say that these people are normal because they have

high uric acid.

So, to consider abnormal as 'associated with the disease' is not accurate all the

time.

Third criteria: Abnormal as Treatable

A person with a disease is treated by antibiotic; can we say he is abnormal?

Sometimes we can and sometimes we cannot, but this criterion says that he is

abnormal.

Considered abnormal when the treatment leads to a better outcome

If removal of risk factor does not remove risk it is not necessary to label people

abnormal

What is considered treatable changes with time.

In the early 50s, folic acid was important to prevent anemia but they found

recently that increased level of folic acid may make problem to the babies or

infants. Thats why, what was considered normal or treatable in the past, is not

necessarily normal now.

For the final material I summari ze the lectures according to the topics rather than each lecture

separated.

DONE BY: AMMAR ANAGREH

Special thanx for our great CR for helping me in this lecture, U R THE BEST LOGAN

THE END

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(6) Abnormalities