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Diagnosis of DementiaMethods for Interpretation of Scores of 5 Neuropsychological Tests

Nachshon Meiran, PhD; Donald T. Stuss, PhD; D. Antonio Guzman, MD;Ginette Lafleche, PhD; Jonathan Willmer, MD

Objective: To provide methods to interpret and com-

pare different neurobehavioral screening tests for the di-agnosis of dementia.

Design: Five mental-status neuropsychological tools fordementia screening were administered to patients in a

memory disorder clinic. These included the Mini-MentalState Examination, the Dementia Rating Scale, the 6-itemderivative of the Orientation-Memory-Concentration Test,a short Mental Status Questionnaire, and a composite toolwe labeled the Ottawa Mental Status Examination, whichassessed orientation, memory, attention, language, and vi-sual-constructive functioning.Results: To obtain z and percentile scores, norms are

for the different tests, computed separately for patientswith dementia of the Alzheimer type, vascular demen-tia, or no dementia. Another set of norms is reported inwhich a test score is translated directly into the posttestprobability of dementia. Translation formulas are givento allow the estimation of the score on one test from theresult on another test.

Conclusion: The interpretation of tests used to diag-nose dementia must be based on an understanding of themeaning of an individual score, which is based on thequestion asked and the population to which the patientis referenced.

Arch Neurol. 1996;53:1043-1054

IN A companion article in thisissue,1 we compared 5 standard¬ized neuropsychological toolsfor diagnosis of dementia. If thegoal is to have a tool for general

screening of dementia, we recommendedthe Mini-Mental State Examination(MMSE) or Orientation-Memory-Concentration (OMC) Test. In additionto having the best statistical qualities,these tests are short. Our research goalsin the present study were to providemethods for interpreting the test scores

and to establish methods for translatingscores obtained on one test into theirequivalent on another test. The same

database that was used by Stuss et al1 was

used as the basis for interpretations. Thegeneral background is described in thatarticle.

RESULTS

In Table 2 through Table 13, we givethe normative data for our sample,which can be used to interpret indi¬vidual test scores and compare different

screening tests for dementia. Unlessstated otherwise, the analyses were per¬formed on the scores of patients whosenative language was French or Englishand who were tested in their native lan¬guage.

MEANS, SDs, ANDPERCENTILE SCORES

In this section, all the patients withDAT (possible and probable) were

joined in a single group to increase thesample size. Means and SDs are neces¬

sary to compute scores. We report themeans and SDs for the overall scores inTable 2. In Table 3, we report the means

for the subscales of Dementia RatingScale (DRS) and Ottawa Mental StatusExamination (OMSE). Raw scores maybe converted to percentile scores basedon the results in Tables 43 through 8.

See Patients and Methodson next page

From the Rotman ResearchInstitute of Baycrest Centre(Drs Meiran and Stuss) andthe Departments of Medicine(Neurology) and Psychology(Dr Stuss), University ofToronto, Ontario; theDepartment of Medicine(Neurology), University ofOttawa, Ontario (Drs Stuss,Guzman, and Willmer); theMemory Disorder Clinic,Elizabeth Bruyere HealthCentre, Ottawa (Drs Guzmanand Willmer); and theDepartment ofNeuropsychology, NewEngland Sinai Hospital andRehabilitation Center,Stoughton, Mass (Dr Lafleche).

at University of Toronto, on May 3, 2011 www.archneurol.comDownloaded from

http://www.archneurol.com

PATIENTS AND METHODS

The patients and diagnostic procedures have been de¬scribed.1 Four types of dementia were considered inthe analysis: probable dementia of the Alzheimer type(DAT), possible DAT, vascular dementia (VaD), anda group of mixed dementia types. Diagnosis of prob¬able and possible DAT was based on National Insti¬tute of Neurological and Communicative Disordersand Stroke-Alzheimer's Disease and Related Disor¬ders Association (NINCDS-ADRDA) criteria.2 The 2most common types of dementia were DAT and vas¬

cular dementia. A fifth group included approxi¬mately one fourth of the patients referred for memoryproblems and suspected Alzheimer disease who were

eventually diagnosed as not having dementia. De¬mographic information is given in Table I.

RELATING TEST SCORES TO LIKELIHOODRATIOS AND POSTTEST PROBABILITY

Perhaps the most desired use of interpretation of testscores is to assign them directly to diagnosis. Using cut¬off scores does precisely that. For example, with a cutoffof 23/24, an MMSE score of 20 is interpreted as patho¬logical, whereas a score of 28 is interpreted as normal.

See also page 1033

Realistically, however, diagnosis always involves uncer¬

tainty, because a given test score might be obtained bypatients with and without dementia. Eventually, the di¬agnostician needs to assign a degree of confidence (or

probability) to the diagnosis. The norms reported in Tables9 to 13 associate each test score with the probability thatthe patient has dementia as opposed to providing a di¬chotomous classification of dementia or no dementia.

Two sets of probabilities are assigned to each score.

One set of probabilities is assigned to the score when it isinterpreted in reference to a group diagnosed as having de¬mentia (pathological). In this situation, the score is con¬

sidered with all scores below it. For example, the score of20 on the MMSE is considered with the scores of 0 to 19.The assigned probabilities reflect the chances that pa¬tients scoring 20 or lower have dementia. For OMC, highscores reflect worse performance, so the score is consid¬ered with the scores above it. The second set of probabili¬ties is assigned to the same score when it is interpreted as

normal. In this situation, it is considered with all the higherscores (or lower scores if the test is OMC). In the same ex¬

ample, the assigned probabilities reflect the chances that a

patient scoring 20 or better on MMSE has dementia.The interpretation of a specific score should be done

in relation to a specific base rate. Consequently, each set ofprobabilities (pathological or normal) is subdivided accord¬ing to the prevalence ofdementia in the population fromwhichthe patient comes. Prevalence has a substantial influence on

the chances ofdementia for a given score. Research has shownthat people, including experienced clinicians, are prone toa number oferrors when they assign probabilities. Most no¬

tably, too much weight is given to the diagnostic value ofthe test (to what degree a given score indicates dementia),and the prevalence of the phenomenon in the reference popu¬lation is underestimated or even ignored completely.4 Forexample, a score of 20 on the MMSE is considered patho¬logical. As our results show, some patients withoutdemen¬tia scored 20 or lower. The chances of encountering such a

person in a clinical diagnostic situation depends on the per¬centage ofpersons without dementia in the population from

Table 1. Demographic Data of the Populations Studied*

Total DAT*

No. (%) of Patients!Probable DAT VaD Mise Nondemented

CDR rating0123

EducationGrade 1-8High schoolUndergraduateGraduate

SexFemaleMale

Age, yMeanRange

4(3)94(61)38(25)18(12)59 (36)72 (42)31 (18)10(6)

116(66)61 (34)71.852-88

36(51)18(25)17(24)26 (32)34 (43)15(19)4(5)

57 (70)24 (30)

71.654-84

3(4)57 (83)7(10)2(3)

33 (42)29 (37)15(19)2(3)

42(52)39 (48)73.349-88

11(7)103 (68)34 (22)5(3)

72 (43)61 (37)24(14)10(6)75 (44)95 (56)70.942-89

82 (59)56(41)

48 (30)65 (40)31 (19)18(11)89 (54)77 (46)61.419-86

*DAT indicates Alzheimer disease; VaD, multi-infarct (vascular) dementia; Mise, those cases diagnosed as dementia with mixed types or types other thanDAT or VaD; Nondemented, those cases referred for suspected dementia but eventually diagnosed as nondemented; and CDR, Clinical Dementia Rating Scale.Percentages are rounded. The total percentage may therefore not equal 100.

Unless otherwise indicated.XTotal DAT includes patients in both the probable and possible classification.



Table 2. Norms for the Computation of Scoresfor the 5 Screening Tests According to Type of Dementia4

Type of Dementia,

Test DAT VaD Mise NondementedMMSE

Mean 17.8 21.5 21.3 27.9SD 7.2 6.0 6.5 2.9No. of patients 164 76 159 146

OMCMean 18.5 14.2 14.2 5.9SD 7.0 6.7 7.1 5.1No. of patients 163 76 158 145

MSQMean 4.8 6.2 6.6 8.9SD 2.9 2.4 2.5 1.3No. of patients 163 76 157 146

DRSMean 106.0 112.6 113.6 132.5SD 22.1 18.0 16.5 11.0No. of patients 124 67 131 142

OMSEMean 22.6 27.6 27.6 36.2SD 9.6 7.8 8.6 3.9No. of patients 163 76 158 146

*MMSE indicates Mini-Mental State Examination; OMC,Orientation-Memory-Concentration Test; MSQ, Mental StatusQuestionnaire; DRS; Dementia Rating Scale; and OMSE, Ottawa MentalStatus Examination. Other abbreviations are given in Table 1.

which the person comes. The higher the proportion ofper¬sons without dementia, the greater the chances of findinga person who did not have dementia with a score of 20 or

lower. In the normal population, multiple factors may con¬

tribute to a lower score (hence the "normal" curve), factorsthat do not imply a dementia. Thus, in a memory disorderclinic, the diagnosis of dementia based on an MMSE score

at or below 20 is associated with high confidence (98%, ac¬

cording to our estimation), because in that setting, the chancesare minimal of finding a person without dementia scoringthat low. In contrast, the associated confidence for the samescore of 20 is only 26% if the diagnosis is made in the com¬

munity and the person is younger than 75 years. Neverthe¬less, most people find this reasoning counterintuitive andwould assign similar degrees of confidence to a diagnosisofdementia regardless of the setting (memory disorder clinicvs the community). These errors in reasoning prevail even

among persons with a background in statistical reasoning.One way to overcome these biases is to relate a given

test score directly to an estimated probability, thereby by¬passing the need to make inferences from test scores. Toachieve this goal, we determined the sensitivity and speci¬ficity corresponding to all possible cutoffpoints on the test.From sensitivity and specificity, we computed 2 likelihoodratios for each test score. The pathological likelihood ratio(PLR) corresponds to scores equal to or worse than the givenscore. Anothervariable was the normal likelihood ratio (NLR),which corresponds to scores equal to or better than the givenscore. Using a likelihood ratio is a convenient way ofapply¬ing the Bayes theorem in a practical setting.

To make use of the norms easy, we computed post-test probabilities that are associated with any given test

Table 3. Norms for the Computation of Scoresfor the Subscales of DRS and OMSE Accordingto Type of Dementia*

Type of Dementia,

Subscales DAT VaD MiseI

NondementedDRS

AttentionMeanSDNo. of patients

InitiationMeanSDNo. of patients

ConstructionMeanSDNo. of patients

ConceptualizationMeanSDNo. of patients

MemoryMeanSDNo. of patients

OMSEOrientation

MeanSDNo. of patients

MemoryMeanSDNo. of patients

AttentionMeanSDNo. of patients

LanguageMeanSDNo. of patients

CopyMeanSDNo. of patients

34.13.9

107

25.07.9

107

5.11.5

107

28.18.7

107

13.15.5

107

7.64.0

137

4.52.2

136

3.72.8

136

6.12.0

137

0.60.5

137

34.63.1

52

27.26.3

52

5.21.6

52

29.67.2

52

15.15.3

52

9.43.7

59

5.61.8

59

4.92.2

59

6.71.4

59

0.60.5

59

35.02.6

95

27.16.7

95

5.11.4

95

30.77.2

95

16.54.9

95

10.03.7

113

5.72.3

112

4.42.7

112

6.41.8

113

0.60.5

113

35.82.5

97

33.54.2

97

5.70.9

97

35.74.7

97

21.44.6

97

13.01.5

100

8.01.6

100

6.31.5

100

7.50.8

100

0.90.2

100

* Abbreviations and test names are given in Tables 1 and 2, respectively.

score. We shall first explain how these probabilities were

computed and then explain how to use the norms.

The computation of posttest probabilities was per¬formed in 4 steps5:

1. Percentages were translated into pretest odds us¬

ing the following formula: odds=percent/(100 —per¬cent) or odds=probability/(l—probability).

According to this formula, 20% is equivalent to oddsof 1 in 4 (1 person with dementia for every 4 persons with¬out dementia). Pretest odds reflect the base rate, or thechances that the person has dementia based only on theknowledge about the population from which the pa¬tient comes.

2. Each test score was associated with PLR and NLR,which are reported in Tables 9 to 13. These values were



Table 4. Percentile Scores on MMSEAccording to Type of Dementia*

Score0-1

23456789

101112131415161718192021222324252627282930

Type of Dementia, Percentile Scores

DAT VaD Nondemented2334678

1013161821252934394449535761657077838791959899

233579

1114202530343843475359647280879297

000000000000000122344579

121725365483

All11123345679

111214172024273034374146515662

6768494

*AII indicates patients with dementia regardless of type. Otherabbreviations and test names are given in Tables 1 and 2, respectively.See Crum et al3 for the effects of age and education. Our nondementedgroup is likely not representative of the normal population. It reflects morethe type of nondemented individual referred to a memory clinic, suggestingthat the scores presented herein may be more helpful for differentialdiagnoses. Underlined scores refer to examples in the text.

computed based on the data in our database. For ex¬

ample, an MMSE score of 18 is associated with a PLR of20 and an NLR of 0.46 (Table 8).

3. Posttest odds were computed. These odds re¬

flect the chance that the patient has dementia and allowfor the base rate and the test score. Posttest odds are theproduct of pretest odds and PLR or NLR. If the score isinterpreted as pathological (the score is considered withthe scores that reflect worse performance), posttestodds=pretest oddsX PLR. If the score is interpreted as nor¬

mal (the score is considered with the scores that reflectbetter performance), posttest odds=pretest oddsXNLR.

Using the values in the example, the correspond¬ing odds for an MMSE score of 18 when interpreted as

pathological are 20X 1/4=5 (5 patients with dementia forevery patient without dementia), and when interpretedas normal are 0.46X1/4=0.115 (0.115 patients with de¬mentia for every patient without dementia).

4. Posttest odds were converted into probabilitiesusing the following formula: probability = odds/(14·odds). For example, the obtained posttest odds of 5correspond to 5/(14-5)=0.83, or 83%.

Table 5. Percentile Scores on OMCAccording to Type of Dementia*

Type of Dementia, Percentile Scores -1

Score DAT VaD Nondemented All

28 9 2 0 427 18 4 0 726 19 5 0 825 22 9 0 1024 29 12 0 1423 33 14 0 1622 36 15 0 1821 42 18 1 2120 47 22 2 2519 48 24 3 2618 51 28 3 2917 56 32 5 3416 62 36 7 3815 64 39 7 3914 67 45 8 4313 72 54 10 4812 77 61 12 5211 79 63 13 5410 87 70 18 61

9 94 78 23 678 96 82 29 727 98 86 37 766 98 89 42 795 99 91 48 814 99 94 56 853 99 97 64 882 99 99 74 911 99 100 84 950 100 100 92 97

*AII indicates patients with dementia regardless of type. Otherabbreviations and test names are given in Tables 1 and 2, respectively.

Table 6. Percentile Scores on MSQAccording to Type of Dementia*

Type of Dementia, Percentile Scores -—r~

Score DAT VaD Nondemented All0 2 0 0 11 10 2 0 42 22 6 0 93 33 11 0 154 44 20 1 215 51 33 2 286 60 45 4 377 71 57 8 468 82 70 16 589 93 87 42 75

10 99 97 81 93


We computed posttest scores associated with some

typical prevalence values and expressed the results in prob¬abilities rather than odds to make them readily interpretable.The prevalence values for which we made these computa¬tions were 2%, which is approximately the prevalence ofde¬mentia in the community for people aged 65 to 74 years.6Ten percent corresponds to the prevalence ofdementia among



Table 7. Percentile Scores on the DRSAccording to Type of Dementia*


Score

22-2627-4950-57

585960616263646566676869707172737475767778798081828384858687888990919293949596979899

DAT122334445566667

999

10111213131415151515161616171718192021232425262829

VaD0.000.00

23333444444444689

101011131616161616161617

Nondemented0.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00

All0.000.00

8999

101112121314141516

Table 7. Percentile Scores on the DRSAccording to Type of Dementia* (cont)

Type of Dementia, Percentile Scores -1

Score DAT VD Nondemented All100 31 19 2 17101 34 19 2 19102 36 19 3 20103 38 20 3 20104 39 24 3 21105 40 28 3 22106 _4_t_ _30 J3 _23107 42 32 3 25108 43 37 3 27109 45 41 4 29110 46 43 4 30111 49 46 4 31112 52 50 4 33113 55 52 5 35114 58 54 6 37115 60 55 7 39116 63 55 8 41117 66 57 8 43118 68 60 10 45119 70 63 12 48120 72 65 14 50121 74 66 14 52122 77 66 15 54123 79 66 17 56124 81 69 18 58125 83 70 19 59126 85 72 20 61127 87 74 23 63128 89 75 24 65129 91 76 25 67130 92 77 28 69131 92 82 30 70132 92 88 32 72133 94 90 35 74134 95 92 38 76135 97 93 42 78136 98 95 48 81137 98 96 56 84138 98 96 65 87139 99 97 73 90140 99 99 78 92141 99 100 83 94142 99 100 89 96143 100 100 95 98144 100 100 99 100

*AII indicates patients with dementia regardless of type. Otherabbreviations and test names are given in Tables 1 and 2, respectively.Underlined scores refer to examples in the text.

medical inpatients who are younger than 84 years; 20% isapproximately the prevalence in the community for personsbetween 74 and 85 years; 50% is approximately the preva¬lence in nursing homes and in the community for personsolder than 85 years; 75% is the prevalence in our sample,which is probably representative of memory disorder clin¬ics. To use the norms, choose the prevalence that you be¬lieve matches the setting you work with and ignore all othercolumns. Two probabilitieswere computed, one for the scoreas considered pathological, the other for the same score con¬

sidered as normal. For example, assume that testing is per-

formed in a nursing home in which prevalence is about 50%.If a patient obtained an MMSE score of 17, the probabilitiesgiven in Table 9 are 96% when the score is considered as

pathological and 34% when it is considered as normal. Inother words, using the assumption ofdementia (left side ofTable 9, pathological), the same score of 17 is interpretedas a diagnosis of dementia (96%). If the score is interpretedas indicating normal performance (right side ofTable 9), thepatient has a 34% chance of having dementia. Hence, thescore probably indicates that this patient has dementia. Thescore can be safely interpreted as pathological (leaving room



Table 8. Percentile Scores on OMSEAccording to Type of Dementia*


Score DAT VaD Nondemented

0-12-3

456789

10111213141516171819202122232425262728293031323334353637383940

23345789

121516182023262932353943485153575960636770757983889295989899

100

2335689

1011131618243034374044485153576168768288939598

100

00000000000000000111122344457

10131518243343628496

All011223445678

101113141617202225272932343740434650555863687480879599


for only 100%—96%=4% chances of error), and cannot beinterpreted safely as normal, because the chances for error

associated with this interpretation are 34%. The table doesnot begin with the lowest possible score and ends before thehighest possible score, because no patients without demen¬tia scored below a given score and no patients with demen¬tia scored above a given score. Scores lower than thoseincluded in the tables (indicating dementia) or higher thanthose indicated in the tables (indicating not having demen¬tia) should, therefore, be associated with high degrees ofcertainty.HOW TO COMPUTE NORMS FOR PREVALENCE

RATIOS THAT ARE NOT INCLUDEDIN THE TABLES

The tables include norms for typical prevalence ratios.However, one may wish to compute norms for a preva-

lence ratio that is not included. For this purpose, PLRand NLR for each test score are reported on the left sideof the tables. To obtain the norms, estimate the preva¬lence of dementia for which you wish to compute thenorms and follow the aforementioned steps 1 to 4.

TAILORED CUTOFF POINTS

The norms can be used to establish cutoff points tai¬lored to a setting (prevalence). To do that, a conventionis needed about the desired certainty of the diagnosis. Weare unaware of an existing convention and, therefore, sug¬gest a convention that considers the diagnostic needs typi¬cal for a given setting. Consider first a positive diagno¬sis. In the community, low levels of confidence (70%)are sufficient to establish a positive diagnosis, becausethe person is likely to be referred for a more extensivefollow-up. In contrast, high levels of confidence shouldbe associated with a positive diagnosis in a memorydisorder clinic. We suggest a level of confidence of atleast 0.95.

For a negative diagnosis, we suggest that the con¬

vention be a probability close to that of persons in a simi¬lar age in the community. A convention for negative di¬agnosis is needed in settings such as nursing homes andmemory disorder clinics, where ruling out the possibil¬ity of dementia is likely to affect important decisions aboutthe person being tested. In other words, to rule out de¬mentia, the test score should be associated with a nor¬

mal (right side of columns in tables) posttest probabil¬ity of about 2% for persons aged 65 to 74 years or 20%for persons older than 74 years.

We provide some examples of how these conven¬

tions are applied. Consider first a positive diagnosis forpersons older than 74 years in the community. Accord¬ing to Siu,6 the prevalence of dementia in the commu¬

nity for persons in the age range 74 to 85 years is about20%. Therefore, the relevant columns are for pathologi¬cal diagnosis (left side) and prevalence of 20% (middlecolumn), where we looked for values close to 0.70. Theseconsiderations led us to suggest the following cutoffs:23/24 for MMSE (because the confidence level for a score

of 23 is 0.72 when prevalence of dementia is 20%, Table9), 15/14 for OMC (confidence level of 0.73 for a score

of 15, Table 10), and 116/117 on DRS (confidence levelof 0.73 for a score of 116, Table 12).

Cutoffs for a memory disorder clinic (prevalenceof 75%) were determined using the convention ofpathological diagnosis with a confidence level of at least0.95. These are 25 or lower for MMSE, 11 or higher forOMC, and f 20 or lower for DRS (Tables 9, 10, and 12,respectively, under Pathological Diagnosis and the col¬umn associated with prevalence of 0.75).

In a memory disorder clinic, a high score can leadto the rejection of the possibility of dementia. If the score

is so high that the probability for dementia associated witha negative diagnosis is as low as that in the community,the patient should be considered as not having demen¬tia. For persons older than 74 years, this rate is about 0.20.Hence, the recommended upper cutoffs are MMSE=27or more (far right column, Table 9, where the associ¬ated posttest probability for normal diagnosis is 0.23),



Table 9. Likelihood Ratios for Pathological and Normal Diagnoses as a Function of MMSE Score and Prevalence*

Diagnosis,! Likelihood Ratio -'-1

Pathological, % Prevalence Normal, % Prevalence^MMSE I-1 |-1Score PLR NLR 2_10 20_50 75_2_10 20 50 75

16 49.0 ... 0.50 0.84 0.92 0.98 0.9917 2 5 052 0360750^7096099 0.01 0.05 0.12 0.34 0.6118 20.0 0.46 0.29 0.69 0.83 0.95 0.98 0.01 0.05 0.10 0.32 0.5819 21.3 0.41 0.30 0.70 0.84 0.96 0.98 0.01 0.04 0.09 0.29 0.5520 17.0 0.37 0.26 0.65 0.81 0.94 0.98 0.01 0.04 0.08 0.27 0.5321 14.2 0.33 0.22 0.61 0.78 0.93 0.98 0.01 0.04 0.08 0.25 0.5022 12.5 0.31 0.20 0.58 0.76 0.93 0.97 0.01 0.03 0.07 0.24 0.4823 10.4 0.27 0.17 0.54 0.72 0.91 0.97 0.01 0.03 0.06 0.21 0.4524 8.50 0.18 0.15 0.49 0.68 0.89 0.96 0.00 0.02 0.04 0.15 0.3525 6.29 0.17 0.11 0.41 0.61 0.86 0.95 0.00 0.02 0.04 0.15 0.3426 J3S 014 O08 033 052 081 093 0.00 0.02 0.03 0.12 0.3027 3.20 0.10 0.06 0.26 0.44 0.76 0.91 0.00 0.01 0.02 0.09 0.2328

...

0.06... ...

...

... ...

0.00 0.01 0.01 0.06 0.15

*Likelihood ratio indicates probability of having Alzheimer disease; PLR, likelihood ratio for pathological result (less than or equal score); and NLR,likelihood ratio for normal result (greater than or equal score). Test names are given in Table 2. Underlined numbers refer to examples in the text.

t The diagnosis of the score as normal is based on scores equal to or better than the given score. A diagnosis of the score as pathological is based onscores worse than or equal to the given score.

^Typical prevalence values are 2% (community ages, 65-74 years), 10% (medical inpatients), 20% (community ages, 74-85 years), 50% (community olderthan 85 years and nursing homes), and 75% (memory clinics).

Table 10. Likelihood Ratios for Pathological and Normal Diagnoses as a Function of OMC Score and Prevalence*

Diagnosis, Likelihood Ratio

Pathological, % Prevalence Normal, % Prevalence

OMC PLR NLR_2_10 20 50 75_2 10 20 50 75

22 43.0 ... 0.47 0.83 0.91 0.98 0.9921 27.5 0.58 0.36 0.75 0.87 0.96 0.99 0.01 0.06 013 0.37 0.6420 18.3 0.46 0.27 0.67 0.82 0.95 0.98 0.01 0.05 0.10 0.32 0.5819 18.3 0.46 0.27 0.67 0.82 0.95 0.98 0.01 0.05 0.10 0.32 0.5818 20.3 0.46 0.29 0.69 0.84 0.95 0.98 0.01 0.05 0.10 0.32 0.5817 11.7 0.40 0.19 0.56 0.74 0.92 0.97 0.01 0.04 0.09 0.29 0.5516 10.4 0.32 0.18 0.54 0.72 0.91 0.97 0.01 0.03 0.07 0.24 0.4915 10.6 0.29 0.18 0.54 0.73 0.91 0.97 0.01 0.03 0.07 0.22 0.4714 7.60 0.28 0.13 0.46 0.66 0.88 0.96 0.01 0.03 0.07 0.22 0.4613 7.27 0.27 0.13 0.45 0.65 0.88 0.96 0.01 0.03 0.06 0.21 0.4512 6.23 0.22 0.11 0.41 0.61 0.86 0.95 0.00 0.02 0.05 0.18 0.4011 6.23 0.22 0.11 0.41 0.61 0.86 0.95 0.00 0.02 0.05 0.18 0.4010 4.13 0.22 0.08 0.31 0.51 0.81 0.93 0.00 0.02 0.05 0.18 0.40

9 4.13 0.06 0.08 0.31 0.51 0.81 0.93 0.00 0.01 0.01 0.06 0.158 2.69 0.06 0.05 0.23 0.40 0.73 0.89 0.00 0.01 0.01 0.06 0157 2.68 0.05 0.05 0.23 0.40 0.73 0.89 0.00 0.01 0.01 0.05 0.136

...

0.02... ... ... ... ...

0.00 0.00 0.00 0.02 0.06

* Test names are given in Table 2. Other abbreviations and definitions are given in Table 9.

OMC=9 or lower (associated probability=0.15, Table 10),and DRS=127 or higher (associated probability=0.21,Table 12).

Assume, for example, that a patient older than 85years is tested in a memory disorder clinic. A score of24 or less on the MMSE would suggest an 89% prob¬ability of having dementia and only 15% of not havingdementia. A score of 24 can occur for many reasons

other than dementia (hence the probability of 15% thatthe patient does not have dementia), but the probabilityof dementia is much higher. A score of 27 or highershould lead to a negative diagnosis. An MMSE score of

26 is uninformative in a memory disorder clinic in thesense that the posttest probability is close to the pretestprobability. Similarly, based on our norms, an OMCscore of 10 and DRS scores between 121 and 126 are

uninformative.

CUTOFF POINTS

We do not recommend the use of cutoff points. Neverthe¬less, cutoff points may be useful in some circumstances.Therefore, we generated a set of recommended cutoffpointsbased on our data.1



Table 11. Likelihood Ratios for Pathological and Normal Diagnoses as a Function of MSQ Score and Prevalence*

Diagnosis, Likelihood Ratior


MSQ PLR NLR 2_10 20 50 75 2_10 20 50 754 57.0

...

0.54 0.86 0.93 0.98 0.995 19.3 0.43 0.28 0.68 0.83 0.95 0.98 0.01 0.05 0.10 0.30 0.566 14.6 0.43 0.23 0.62 0.78 0.94 0.98 0.01 0.05 0.10 0.30 0.567 7.64 0.28 0.13 0.46 0.66 0.88 0.96 0.01 0.03 0.07 0.22 0.468 4.00 0.18 0.08 0.31 0.50 0.80 0.92 0.00 0.02 0.04 0.15 0.359 1.60 0.15 0.03 0.15 0.29 0.62 0.83 0.00 0.02 0.04 0.13 0.31

10 ... 0.03 ... ... ... ... ... 0.00 0.00 0.01 0.03 0.08


CONVERSION FORMULAS

Conversion formulas are required when a patient is re¬

ferred from a clinic that does not use the same screeningtest. The results of the principal component analysis showthat conversion of scores is justified, because all the testsmeasure the same process or set of processes. Unfortu¬nately, our results and those of others5 indicate that theformulas are not sufficiently precise to be applicable forthis purpose. The formulas can, nevertheless, be used to

compare between groups of patients, eg, to compare be¬tween studies that used different screening tools. Previ¬ously published conversion formulas that were based on

patients with Alzheimer disease can be applied only tosimilar patients. The conversion formulas that we com¬

puted are given in Table 14. Results from previous stud¬ies are given in Table 1 5.7"9

Before estimating these regression formulas, we testedfor the possibility that the linear relations between anypair of tests are mediated by type of dementia. This was

accomplished by predicting a score on one test by de¬mentia type, the other test, and the difference in regres¬sion weights between dementia types. The difference inregression weights did not reach significance when OMCwas not involved and reached significance in every case

when OMC was involved.For this reason, we estimated the regression formu¬

las on the entire sample and estimated separate formu¬las for different dementia types when OMC was in¬volved in the model. About 70% of the population shouldhave their true score in the range of predicted score plusor minus root mean square error. As one may see, rootmean square error (RMSE) values are large and make itimpractical to use the formulas on individual patients.

USE OF THE TEST RESULTS

The mental-status tests used were the MMSE, the OMCTest, the Mental Status Questionnaire (MSQ), the DRS,and the OMSE.1 They were given in the standard format,with the exception that Canadian content replaced Ameri¬can content (eg, "What is the name of the prime ministerof Canada?")· The results can be used for 2 general pur¬poses: to interpret individual scores and to compare scores

of a patient on one test with his or her score on anothertest. We give examples to assist in using the norms.

INTERPRETATION OF INDIVIDUALTEST SCORES

Does a score of 26 on the MMSE indicate that the pa¬tient has dementia? To answer this question, 4 differentways to interpret an individual test score are given. Themethod selected often relates to the specific diagnosticquestion being asked.

One procedure is to convert the individual score intoa standard (z) score so that comparison can be made on

a linear scale. In a standard score, zero is the mean, andabout 70% of the scores fall between

—

1 and -I-1. A score is obtained by subtracting the group mean score

from the individual score of 26, and then dividing thisresult by the group SD. For example, for the patients inour sample who had DAT, the mean and SD of the MMSEare 17.8 and 7.2, respectively (Table 2). The score forthe patient with a score of 26 is (26—17.8)/7.2= 1.1. Thisresult indicates that the patient's score is more than 1 SDabove the mean of patients with DAT. A score also can

be computed for a different comparison group, suchas the patients without dementia (mean=27.9, SD=2.9,Table 2). In this comparison, the patient's score is(26-27.9)/2.9= —0.7. Using this comparison group, thepatient's score is below the average of patients withoutdementia. This test score, then, interpreted against thebackground of 2 different comparison groups, suggeststhat the patient should be observed, or that other causes

for decreased performance be sought. The performanceis less than expected for a normal population but not rep¬resentative of most patients with DAT.

A second approach is to convert the raw score to per-centiles. This approach addresses the question, How manypatients score worse than or as well as that patient (Table4)? The 87% of patients with DAT have a score worse

than or equal to 26 on the MMSE. In contrast, the samescore is low among patients without dementia, only 17%of whom score equal to or worse than 26. The interpre¬tation is similar to that for the score. Percentiles maybe easier to understand.

A third approach to interpret a test score is to re¬late it directly to the probability that the patient has de¬mentia. To do this, the interpreter needs to know theprevalence of dementia among the patients tested, be¬cause a true estimate of probability depends on the baserate in a defined population. That is, in establishing di-



Table 12. Likelihood Ratios for Pathological and Normal Diagnoses as a Function of DRS Score and Prevalence*

Diagnosis, Likelihood Ratio


DRS PLR NLR 10 20 50-1

75 10 20 50 75737475767778798081828384858687888990919293949596979899

100101102103104105106107108109110111112113114115116117118119120121122123124125126127128129130131132133134135

13.015.016.016.018.020.020.022.022.022.022.024.024.024.024.024.024.024.027.027.027.029.033.033.033.036.018.019.014.015.015.015.015.016.316.312.813.814.014.515.512.810.78.389.388.446.916.156.005.675.444.834.834.794.234.044.003.563.313.102.822.652.51

0.880.860.850.850.830.810.810.790.790.790.790.770.770.770.770.770.770.770.740.740.740.720.680.680.680.650.650.630.600.570.570.570.570.530.530.510.470.460.440.400.380.380.360.270.260.270.230.190.180.150.160.160.110.090.090.050.050.060.060.060.030.03

0.210.230.250.250.270.290.290.310.310.310.310.330.330.330.330.330.330.330.360.360.360.370.400.400.400.420.270.280.220.230.230.230.230.250.250.210.220.220.230.240.210.180.150.160.150.120.110.110.100.100.090.090.090.080.080.080.070.060.060.050.050.05

0.590.620.640.640.670.690.690.710.710.710.710.730.730.730.730.730.730.730.750.750.750.760.790.790.790.800.670.680.610.620.620.620.620.640.640.590.600.610.620.630.590.540.480.510.480.430.410.400.390.380.350.350.350.320.310.310.280.270.260.240.230.22

0.760.790.800.800.820.830.830.850.850.850.850.860.860.860.860.860.860.860.870.870.870.880.890.890.890.900.820.830.780.790.790.790.790.800.800.760.770.780.780.790.760.730.680.700.680.630.610.600.590.580.550.550.540.510.500.500.470.450.440.410.400.39

0.930.940.940.940.950.950.950.960.960.960.960.960.960.960.960.960.960.960.960.960.960.970.970.970.970.970.950.950.930.940.940.940.940.940.940.930.930.930.940.940.930.910.890.900.890.870.860.860.850.840.830.830.830.810.800.800.780.770.760.740.730.72

0.980.980.980.980.980.980.980.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.990.980.980.980.980.980.980.980.980.980.970.980.980.980.980.970.970.960.970.960.950.950.950.940.940.940.940.930.930.920.920.910.910.900.890.890.88

0.020.020.020.020.020.020.020.020.020.020.020.020.020.020.020.020.020.020.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.010.000.000.000.000.000.000.000.000.000.000.000.000.000.000.000.00

0.090.090.090.090.080.080.080.080.080.080.080.080.080.080.080.080.080.080.080.080.080.070.070.070.070.070.070.070.060.060.060.060.060.060.060.050.050.050.050.040.040.040.040.030.030.030.020.020.020.020.020.020.010.010.010.010.010.010.010.010.000.00

0.180.180.180.180.170.170.170160160.160.160.160.160.160.160.160.160.160160.160.160.150.150.150.150.140.140.140.130.120.120.120.120.120.120.110.110.100.100.090.090.090.080.060.060.060.050.050.040.040.040.040.030.020.020.010.010.010.010.010.010.01

0.470.460.460.460.450.450.450.440.440.440.440.440.440.440.440.440.440.440.430.430.430.420.400.400.400.390.390.390.380.360.360.360.360.350.350.340.320.320.310.290.280.280.260.210.210.210.190.160150130.140.140.100.080.080.050.050.060.060.060.030.03

0.730.720.720.720.710.710.710.700.700.700.700.700.700.700.700.700.700.700.690.690.690.680.670.670.670.660.660.650.640.630.630.630.630.610.610.600.590.580.570.550.530.530.520.450.440.450.410.360.350.310.320.320.250.210.210.130.130.150.150.150.080.08

" Test names are given in Table 2. Other abbreviations and definitions are given in Table 9.



Table 13. Likelihood Ratios for Pathological and Normal Diagnoses as a Function of OMSE Score and Prevalence*

Diagnosis, Likelihood Ratio -—I

Pathological, % Prevalence Normal, % Prevalence -1

^—^—

IOMSE PLR NLR 2_II)_20 50 75_2 10 20_50_7ji_

19 42.0...

0.46 0.82 0.91 0.98 0.9920 51.0 0.59 0.51 0.85 0.93 0.98 0.99 0.01 0.06 0.13 0.37 0.6421 57.0 0.49 0.54 0.86 0.93 0.98 0.99 0.01 0.05 0.11 0.33 0.6022 61.0 0.43 0.55 0.87 0.94 0.98 0.99 0.01 0.05 0.10 0.30 0.5623 305 0.39 0.38 0.77 0.88 0.97 0.99 0.01 0.04 0.09 0.28 0.5424 21.7 0.40 0.31 0.71 0.84 0.96 0.98 0.01 0.04 0.09 0.29 0.5525 22.7 036 032 0.72 0.85 0.96 0.99 0.01 0.04 0.08 0.26 0.5226 17.3 0.33 0.26 0.66 0.81 0.95 0.98 0.01 0.04 0.08 0.25 0.5027 17.3 0.32 0.26 0.66 0.81 0.95 0.98 0.01 0.03 0.07 0.24 0.4928 14.4 0.32 023 0.62 0.78 0.94 0.98 0.01 0.03 0.07 0.24 0.4929 12.3 0.29 0.20 0.58 0.76 0.92 0.97 0.01 0.03 0.07 0.22 0.4730 9.63 0.28 0.16 0.52 0.71 0.91 0.97 0.01 0.03 0.07 0.22 0.4631 6.67 0.25 0.12 0.43 0.63 0.87 0.95 0.01 0.03 0.06 0.20 0.4332 5.79 0.23 0.11 0.39 0.59 0.85 0.95 0.00 0.02 0.05 0.19 0.4133 5.38 0.22 0.10 0.37 0.57 0.84 0.94 0.00 0.02 0.05 0.18 0.4034 4.52 0.17 0.08 0.33 0.53 0.82 0.93 0.00 0.02 0.04 0.15 0.3435 3.54 0.06 0.07 0.28 0.47 0.78 0.91 0.00 0.01 0.01 0.06 01536 2.61 0.01 0.05 0.22 0.39 0.72 0.89 0.00 0.00 0.00 0.01 0.0337 ... 0.02

...

... ... ...

...

0.00 0.00 0.00 0.02 0.06


agnostic probabilities, the interpretation of a given testscore must depend on the general environment of the pa¬tient. To make this estimation, one can use recent re¬

views of epidemiological studies.6 For example, accord¬ing to Siu,5 the prevalence of dementia in nursing homesis about 50% (Table 9). If the tester wishes to interpretthe score (score, 26) of a person who is in a nursing homeas a probability of that score reflecting dementia (in whichcase 26 is considered with all the scores below it), thechances are 81% that the patient has dementia. If the score

is to be interpreted as probabilities of reflecting normalfunctioning (and is considered with all the scores aboveit), the chance is 12% that the patient has dementia (Table9). A score of 26, interpreted in the context of the baserate of the specific population, in this case the nursinghome, is interpreted differently than if it is interpretedabsolutely, or even if interpreted against another base rate.For example, the prevalence of dementia in ambulatorymemory disorder clinics is suggested as about 75%. Inthat setting, a score of 26 probably indicates dementiawith a 93% probability. Even if the score was inter¬preted as indicating normal performance (in which case

it was considered with the scores above it), the patientstill would have a 30% chance of having dementia.

Finally, the score can be interpreted as indicating de¬mentia or normal functioning based on a cutoff. This ap¬proach is widely used but oversimplifies the situation (eg,by ignoring the prevalence of dementia in the given set¬

ting).

According to this approach, a score of 26 should notbe interpreted as indicating DAT (Table 3) because it isabove the 23/24 cutoff generally used. However, if the cut¬off is determined to differentiate normal performance fromdementia in general (when all forms of dementia are in¬cluded), the cutoff is positioned higher—28/29. A pa¬tient scoring 26 now would be considered as having de-

mentía, because the score falls below the cutoff. Hence,the cutoff approach suggests that the patient has demen¬tia but the dementia probably is not DAT.

In summary, a score of 26 on the MMSE is inter¬preted as high compared with a sample ofpatients who haveDAT, and low when compared with patients who do nothave dementia (based on scores and percentiles). It can¬

not lead to a definite diagnosis in a setting in which theprevalence of dementia is 50% or lower, but should be in¬terpreted as associated with dementia in a setting in whichmost of the patients have dementia. Finally, according tothe cutoff approach, the score is not associated with DATbut is associated with dementia in general, suggesting con¬

sideration of other forms of dementia. These examples il¬lustrate how one score may be interpreted in many differ¬ent ways to provide answers to different clinical questions.

COMPARING SCORESON DIFFERENT TESTS

When a patient is referred from another clinic that uses a

different mental-status test than the one used by the ac¬

cepting clinic, 2 approaches may be used. One is to trans¬late the score on the other test into scale-free units such as

scores (Table 2) or percentile score (Tables 4 through 8).Another approach is to use a conversion formula. For ex¬

ample, assume your clinic uses MMSE. A patient was re¬

ferred to you with a DRS score of 106, and you wish to knowthe equivalent of 106 on the MMSE. Convert the DRS score

into a score using the norms provided herein. If the com¬

parison is with patients who have DAT, is computed as

0, and performance would be considered average for suchpatients. If the comparison is with patients without de¬mentia (Table 2), z=

~

2.4, well below average. Using per¬centiles, the corresponding percentile scores are 41 and 3



Table 14. Conversion Formulas to Derive Estimated Scores in 1 Test Based on Scores From Another Test*

Test Formula No. of Patients RMSE

DRS

OMSE

MMSEf

OMC

MSQ

45.1+2.34XOMSE45.5+3.01 XMMSE141.3-2.08XOMC69.5+6.54XMSQ-5.5+0.31 x DRS-.5+1.31 XMMSE41.9-1.02 7.9+3.08XMSQ-3.1±023xDRS

.

9+O74X0MSE32.0-0.75XOMC7.0+2.26XMSQ42.8-0.27XDRS35.0-0.76XOMSE35.0-O99XMMSE30.1-2.54XMSQ-3.1+0.09XDRS-1.3+0.28XOMSE-1.3+0.36XMMSE10.7-3.1 xOMC

Based on the Entire Sample.72.69.55.58.72.98.78.86.69.98.74.81.55.78.74.78.58.86.81.78

464466464463464545544544466546545545464544545543463544545543

10.511.113.413.0

3.81.44.43.53.01.13.63.14.83.84.13.81.51.11.21.3

OMC

MMSEDRSMSQOMSE

Based on Patients With DAT (Possible and Probable)33.4-083XMMSE .74 16337.2-0.20XDRS .50 12328.7-2.11 xMSQ .77 16333.0-0.64XOMSE .79 16334.3-O89X0MC .74 163147.1-2.54X0MC .50 12311.6-0.37XOMC .77 16345.32-1.23XOMC .79 163

3.54.43.63.23.7

15.71.44.4

OMC

MMSEDRSMSQOMSE

32.6-0.86XMMSE35.6-0.20XDRS28.2-2.26XMSQ33.6-OJOxOMSE31.2-0.68XOMC135.6-1.76XOMC10.3-0.29XOMC40.9-O94X0MC

Based on Patients With VaD.59.35.66.66.59.35.66.66

7667767676677676

4.44.94.04.03.9

14.61.44.6

OMC

MMSEDRSMSQOMSE

35.3-1.05XMMSE45.4-0.30XDRS30.6-2.77XMSQ37.3-.87XOMSE29.9-0.34XOMC140.5-1.37XOMC9.9-017XOMC39.1-0.50XOMC

Based on Nondemented Patients.36.41.46.43.36.41.46.43

145141145145145141145145

4.14.03.83.92.38.50.92.9

*RMSE indicates root mean square error. Other abbreviations and test names are given in Tables 1 and 2, respectively,jExample.

(compared with patients who have DAT and who do nothave dementia, respectively). These results suggest that thepatient has DAT, because he or she performs at a level thatis typical for patients who have DAT. The third possibilityis to use the conversion formulas in Table 14: MMSE (es¬timated MMSE score=3.1(+0.23XDRS score). The esti¬mated MMSE score would be -3.K+0.23 X106). The ex¬

pected error associated with this formula (RMSE on the rightside of the formulas in Table 14) is 3.0. In other words, basedon the DRS score, the patient is predicted to have an MMSEscore of 21 ±3.

COMMENT

The diagnosis of dementia relies on clinical evaluation,which includes assessment of mental status. Standard¬ized neuropsychological tools are among the many sources

of data clinicians use as supposedly objective indexes ofmental status. It is clear, however, that the results of thesetests may not be interpreted to the best advantage of thepatients, in another article,1 we compared 5 standard¬ized tools for diagnosis of dementia, 3 of which are com-



Table 15. Conversion Formulas From Previous Studies*

Test Formula ReferenceMMSE 28-0.717XIMC "1 Tha| pt a|7+IMC 39.1-1.39XMMSE J ™" al T

IMC 33.51-0.972xMMSE"IMC 38.13-0.219XDRSMMSE 34.5-1.03XIMCMMSE -12.72+0.31 xDRSDRS 174.11-4.57xi.MCDRS 41.53+3.26XMMSE

.MMSE -1O0+0.29XDRSDRS 33.86+3.39XMMSE

.

Salmon et atsí

Bobholz and Brandt9§

* Test names are given in Table 2. IMC indicates Information-Memory-Concentration Test (another version of the Blessed Test).

\N=40 patients with Alzheimer disease.%N=92 patients with probable Alzheimer disease.§N=50 referred patients.

monly used. In this article, we provide various practicalmethods to interpret the test scores of these 5 tests.

The value of these norms as the basis for the differ¬ent interpretations are the large sample size, the interratervalidation of the diagnoses, and the longitudinal follow-up. Three types of interpretation methods are reported. Thefirst set of methods allow comparison of a given test score

with those obtained by several reference populations, suchas patients with possible and probable DAT, patients withvascular dementia, and patients who eventually were di¬agnosed as not having dementia. This approach enables theclinician to determine if the patient being tested per¬formed better or worse than patients in these referencepopulations. Under this heading, we include methods forderiving scores and percentile scores.

A second set of methods allows for the translationof a test score directly to diagnosis. The cutoff approachis one such method. If the score obtained by the patientindicates worse performance than the cutoff score, thepatient is diagnosed as having dementia. If the patientperforms better than the cutoff score, the diagnosis ex¬

cludes dementia. We do not recommend this approach,because using a single cutoff level fails to consider theprevalence of dementia in the population being tested.Furthermore, using cutoffs may leave the clinician withthe illusion that diagnosis is certain.

We suggest a new approach that considers the preva¬lence ofdementia in the patient's representative population.Each test score is associated with the probability or likeli¬hood that the patient has dementia. Testers may use theseprobabilities directly or use them to create tailored cutoffvalues according to the guidelines we provided. Caution isrequired when the prevalence of dementia is high or low.Finally, we suggested several methods for the translation ofa score in one test to its equivalent in another test. These lastmethods are not sufficiently precise to be applied to indi¬vidual data but can be used to compare between groups ofpatients. Moreover, the conversion formulas are applicableonly to a setting similar to ours (a memory disorder clinic)in which the prevalence rate of dementia is 75%.

In computing these norms for the posttest prob¬abilities (Tables 9 through 13), we used patients withprobable DAT as representative of patients with demen-

tia. This results in better sensitivity and specificity of thetests than would be obtained if all types of dementia were

pooled. In this group, the diagnosis of dementia is madewith higher certainty than in the other types of demen¬tia. Hence, PLR and NLR and prevalence were based ondifferent types of dementia. The PLR and NLR were basedon probable DAT and prevalence was based on all thedementias. Some users of the norms may find it unrea¬

sonable and may wish to generate their own probabili¬ties based on the LNR and PNR that we report and theestimated likelihood of probable DAT in their setting.

The diagnosis of dementia is a clinical problem asthe population ages. Standard neuropsychological testsoften are used to assist in these diagnoses. The interpre¬tation of the tests must be based on sufficiently large da¬tabases, and with the understanding of the meaning ofan individual score depending on the question asked andthe population to which the patient is referenced. We haveaddressed the diagnosis of general classifications of de¬mentia. A similar approach is necessary as we identifydementia subtypes, even within a general classification.

Accepted for publication May 10, 1996.This project was funded by a Rotman Research Fellow¬

ship, Toronto (Dr Meiran), research grants from the Alzhei¬mer Society of Canada, the Ontario Mental Health Founda¬tion, and the Medical Research Council ofCanada (Dr Stuss),the Alzheimer Society of Ottawa—Carleton (infrastructurefundsfor the Memory Disorder Clinic at the Ottawa GeneralHospital), and the Ottawa (Ontario) General Hospital.

We are grateful to the patients of the clinic and theirfamilies, to the many staff members of the clinic who were

necessary to the clinic's success in attracting patients, andto the neuropsychology interns and technicians who helpedto test the patients.

Reprints: Donald T. Stuss, PhD, Rotman Research In¬stitute, Baycrest Centre for Geriatric Care, 3560 BathurstSt, Toronto, Ontario, Canada M6A 2E1.

REFERENCES

1. Stuss DT, Meiran N, Guzman DA, Lafleche G, Willmer J. Do long tests yield amore accurate diagnosis of dementia than short tests? a comparison of 5 neu-ropsychological tests. Arch Neurol. 1996;53:1033-1039.

2. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan M. Clinicaldiagnosis of Alzheimer's disease: report of the NINCDS-ADRDA work groupunder the auspices of Dept of Health and Human Services Task Force on Alz-heimer's Disease. Neurology. 1984;34:939-944.

3. Crum RM, Anthony JC, Bassett SS, Folstein MF. Population-based norms forthe Mini-Mental State Examination by age and educational level. JAMA. 1993;269:2386-2391.

4. Bar-Hillel M. The base-rate fallacy in probability judgements. Acta Psychol (Amst).1980;44:211-233.

5. Baldessarini RJ, Finkelstein S, Arana GW. The predictive power of diagnostic testsand the effect of prevalence of illness. Arch Gen Psychiatry. 1983;40:569-573.

6. Siu AL. Screening for dementia and investigating its causes. Ann Intern Med.1991;115:122-132.

7. Thal LJ, Grundman M, Golden R. Alzheimer's disease: a correlational analysisof the Blessed Information-Memory-Concentration Test and the Mini-MentalState Examination. Neurology. 1986;36:262-264.

8. Salmon DP, Thai LJ, Butters N, Heindel WC. Longitudinal evaluation of de-mentia of the Alzheimer type: a comparison of three standardized mental sta-tus examinations. Neurology. 1990;40:1225-1230.

9. Bobholz JH, Brandt J. Assessment of cognitive impairment: relationship of theDementia Rating Scale to the Mini-Mental State Examination. J Geriatr Psy-chiatry Neurol. 1993;6:210-213.



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