Open Access

The screen for cognitive impairment in psychiatry: diagnostic-specific standardization in psychiatric ill patients

  • Juana Gómez-Benito1, 2,
  • Georgina Guilera1, 2Email author,
  • Óscar Pino3, 1,
  • Emilio Rojo3,
  • Rafael Tabarés-Seisdedos4,
  • Gemma Safont5,
  • Anabel Martínez-Arán6,
  • Manuel Franco7,
  • Manuel J Cuesta8,
  • Benedicto Crespo-Facorro9,
  • Miguel Bernardo5,
  • Eduard Vieta6,
  • Scot E Purdon10,
  • Francisco Mesa11,
  • Javier Rejas12 and
  • the Spanish Working Group in Cognitive Function
BMC Psychiatry201313:127

https://doi.org/10.1186/1471-244X-13-127

Received: 22 March 2013

Accepted: 23 April 2013

Published: 6 May 2013

Abstract

Background

The Screen for Cognitive Impairment in Psychiatry (SCIP) is a simple and easy to administer scale developed for screening cognitive deficits. This study presents the diagnostic-specific standardization data for this scale in a sample of schizophrenia and bipolar I disorder patients.

Methods

Patients between 18 and 55 years who are in a stable phase of the disease, diagnosed with schizophrenia, schizoaffective disorder, schizophreniform disorder, or bipolar I disorder were enrolled in this study.

Results

The SCIP-S was administered to 514 patients (57.9% male), divided into two age groups (18–39 and 40–55 years) and two educational level groups (less than and secondary or higher education). The performance of the patients on the SCIP-S is described and the transformed scores for each SCIP-S subtest, as well as the total score on the instrument, are presented as a percentile, z-score, T-scores, and IQ quotient.

Conclusions

We present the first jointly developed benchmarks for a cognitive screening test exploring functional psychosis (schizophrenia and bipolar disorder), which provide increased information about patient’s cognitive abilities. Having guidelines for interpreting SCIP-S scores represents a step forward in the clinical utility of this instrument and adds valuable information for its use.

Keywords

SCIP-S Standardization data Norms Schizophrenia Bipolar I disorder

Background

Cognitive deficits are highly prevalent in psychotic disorders [1], including schizophrenia, bipolar disorder, and schizoaffective disorder [26]. Numerous studies suggest that patients with severe psychiatric disorders have impaired sustained attention [7] and memory [810]. A wide spectrum of executive deficits have also been described, including problems performing goal-oriented tasks, recognizing priority patterns, and planning [11, 12], along with diminished verbal fluency [13] and information processing speed [14, 15]. Increasing recognition that psychosocial prognosis is directly related to the severity of the cognitive impairments [1619], has resulted in a paradigm shift that may expand the targets for treatment beyond the mere symptom suppression and necessitate an integration of cognitive assessment into routine psychiatric practice.

The importance on the field of this study is emphasized by a long-standing initiative of the National Institute of Mental Health, known as MATRICS [20, 21], which has now been subdivided into three different programs: CNTRICS [22], TURNS [23], and TENETS (Treatment and Evaluation Network for Trials in Schizophrenia). The aim of these initiatives is to unify and standardize the types of deficits to be measured and the tests to use, with the final objective of developing effective new treatments for the neurocognitive deficits that occur in these patients. Recently, the MATRICS initiative has proposed a consensus battery that takes between 60 and 90 minutes to administer and is composed of 10 paper-and-pencil tests specifically for cognitive assessment of patients with schizophrenia – the MATRICS Consensus Cognitive Battery [24, 25]. Given the difficulties of performing an assessment lasting more than one hour in standard clinical practice, in the past few decades, considerable effort has been made to create brief cognitive batteries that facilitate an overall understanding of the individual’s cognitive status, without overly sacrificing the sensitivity and specificity of these new instruments. Some examples are the Cognistat, before 1995 known as the Neurobehavioral Cognitive Status Examination [26], the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) [27], the Woodcock-Johnson III Test of Cognitive Abilities (WJ III COG) [28], and the Brief Assessment of Cognition in Schizophrenia (BACS) [29]. These instruments have decreased the time it takes to assess patients to about 40–50 minutes, but even so they have a high cost in terms of time and economics due to time constraints on practitioners in their daily clinical practice.

More recently, other types of studies have focused on the development of cognitive screening tools – scales that do not require additional materials in order to be administered, tools that have different interchangeable versions, tools that are simple and easy to administer, and have an administration time that is appropriate and manageable in clinical practice, i.e., approximately 15 minutes. Some examples are the Brief Cognitive Assessment (BCA) [30], the Screen for Cognitive Impairment in Psychiatry (SCIP) [31], and the Brief Cognitive Assessment Tool for Schizophrenia (B-CATS) [32]. All of these have good psychometric properties [3035], but still no standardization data have been established for any of them.

A Spanish translation of the SCIP was recently introduced (SCIP-S) [36] which demonstrated appropriate psychometric properties both for patients with a diagnosis of schizophrenia [34] and those with bipolar I disorder [33], with regard to equivalence between parallel forms, internal consistency, temporal stability, dimensional structure, and convergent validity. Tentative cut-scores for identification of significant cognitive impairment irrespective of diagnosis are available [35], but the resulting binary classification is insufficient for description of the severity of identified impairment relative to a patient’s clinical cohort after adjustment for age, gender, and education. Guidelines for the interpretation of the SCIP-S would thus represent a step forward in the clinical utility of this instrument and add valuable information on its proper use.

Normative data represent performance on a measure or test by a standardization sample against which other performances on the measure can be compared [37]. Lack of normative data limits the interpretation of scores in individual cases as well as in treatment outcome research (as we cannot know if a score is typical, high or low for the population being studied) [38]. This has implications for our ability to assess the clinical significance of a score (or change in a score). Norm scores can assist clinicians in providing quantitative labels for the degree to which a raw score is to be considered average, elevated, or extreme and might be useful for diagnostic purposes, clinical decision making, or evaluation of treatment effects [37]. A traditional approach to deriving norm scores is to compare an individual’s raw score to a reference group with the same condition matched for background variables such as age and gender. In addition, clinicians using norms for comparison can more readily interpret a patient’s performance on a number of relevant self-report dimensions as well. This should assist in the determination of whether or not an individual’s responses are unusual for someone experiencing, in this case, cognitive deficits. In turn, this may suggest possible courses of action, such as further investigation or treatment (whose outcome can be evaluated against the normative dataset) [37, 38].

When evaluating cognitive function in routine practice, clinicians usually compare the patient score against the norms in the general healthy population to ascertain whether the patient cognitive function is preserved or impaired. In such case, comparison allows to determine the distance to what a particular patient separate from the mean score. Nevertheless, in many cases the practitioner refers the patient to a specialist for formal recognition when his/her performance is unusually low compared with patients with same condition. Particularly when additional etiologies (in addition or replacement of schizophrenia) responsible for the high cognitive impairment observed is suspected [39]. Concerning this point, patient score should be compared with the norms belonging to subjects with the same health condition to assess how the different is the patient scoring related to his/her population of reference. As stated by Irvison et al. [40], this information can improve the clinician´s understanding of patient´s cognitive strength and weakness, put a patient’s cognitive abilities into perspective for their diagnosis, and facilitate multidisciplinary treatment decisions.

In this context, to date there are no standardization data for the SCIP-S scale in psychiatric patients that allow the examiner to interpret the patient scores relative to the cognitive performance of their peers. Thus, the objective of this study is to provide the first clinical normative data for the SCIP-S in patients with functional psychosis, and specifically with schizophrenia-spectrum disorder or bipolar I disorder.

Methods

Participants

Patients diagnosed criteria with schizophrenia, schizoaffective disorder, schizophreniform disorder, or bipolar I disorder according to DSM-IV-TR [41] were enrolled in this study. To take part, patients had to be between 18 and 55 years of age and in a stable phase of the disease. In the case of patients with schizophrenia spectrum disorders, stability was defined by: a) no hospitalization in the past 3 months, and b) a total score of less than 70 on the Positive and Negative Syndrome Scale (PANSS) [42, 43]. In the case of patients with bipolar I disorder, stability was defined as: a) 6 or more months in remission, b) a score less than or equal to 7 on the 17-items Hamilton Depression Scale (HAM-D) [44], and c) a score less than or equal to 6 on the Young Mania Rating Scale (YMRS) [45]. We excluded individuals that were participating in a clinical trial, and those with a serious medical or neurological condition, another psychiatric disorder as a primary diagnosis or main reason for treatment, major depression, or difficulty reading and/or writing. The process of recruitment began with a consensus conference on the diagnostic criteria for the different schizophrenia spectrum disorders and the bipolar disorder I. This consensus was adopted by all participating psychiatrists. This conference dealt mainly with the standard psychiatric interview based on the DSM-IV diagnostic criteria (anamnesis and the exploration of the mental condition), the PANSS, HAM-D, and YMRS scales, and the different inclusion/exclusion criteria of our study.

Instrument

The SCIP [31] is a brief screening tool designed to assess cognitive impairment in psychiatric patients. It has five subtests for evaluating immediate (Verbal Learning Test-Immediate; VLT-I) and delayed (Verbal Learning Test-Delayed; VLT-D) verbal learning, working memory (Working Memory Test; WMT), verbal fluency (Verbal Fluency Test; VFT), and processing speed (Processing Speed Test; PST). It may be administered without the need for additional equipment, i.e., a pencil and a stopwatch, and requires nearly 15 min. Three alternative forms of the scale are available to facilitate repeated testing. Table 1 contains the description and the main characteristics of the SCIP subtests.
Table 1

Description of the SCIP subtests

Subtest

Description

Score

Range scores

VLT-I

Three trials of a 10 word list-learning task with immediate recall after each list presentation

Sum of the number of words correctly recalled over the three trials

0-30

WMT

Eight 3-letter combinations of consonants, with two trigrams each assigned to a 0, 3, 9, or 18 second delay with backward counting distraction.

Sum of the letters correctly recalled

0-24

VFT

Two trials of 30 seconds during which the subject is asked to generate words that begin with a given letter of the alphabet under some specific rules

Sum of acceptable words over the two trials

≥ 0

VLT-D

Delayed recall test of the VLT-I words

Sum of the number of words correctly recalled

0-10

PST

Task that in 30 seconds requires the subject to translate the Morse code equivalents of six letters from the alphabet in boxes under a randomly distributed sequence of the letters

Sum of the number of correct sequential translations

0-30

VLT-I = Verbal Learning Test-Immediate; WMT = Working Memory Test; VFT = Verbal Fluency Test; VLT-D = Verbal Learning Test-Delayed; PST = Processing Speed Test.

The psychometric properties of the SCIP were studied in a sample of patients with schizophrenia [34] and in a sample of bipolar I patients [33], and were shown to be adequate. Specifically, both studies demonstrated the equivalence among the three parallel forms, internal consistency (Cronbach’s alpha of 0.73 and 0.74, respectively), and test-retest reliability (intraclass correlation coefficient of 0.90 and 0.87, respectively, for the SCIP total score). Convergent validity was supported by the associations between SCIP subtests and conventional neuropsychological instruments applied in routine clinical practice. The scores also converged on a single cognitive factor accounting for around 50% of the total variance, suggesting a one-factor internal structure in both samples named cognitive performance. Finally, when comparing cognitively-impaired individuals and those with adequate functioning, the proposed cut-off point of the SCIP (< 70) was associated with a sensitivity of 87.9 and specificity of 80.6.

Procedure

This study was approved by the University of Barcelona Ethics Committee. The SCIP-S was administered to all patients, who were systematically tested once it was confirmed that they met the study inclusion criteria and gave their written informed consent to voluntarily participate in the study; data confidentiality was maintained at all times. The data were collected at 119 Spanish mental health centers, selected by probability sampling adjusted by population weights from the 17 Spanish Autonomous Communities, with the participation of 132 psychiatrists duly trained in administering the instrument with a video designed for that purpose. Before the start of the process, a neuropsychologist experienced in administration of neuropsychological tests and batteries trained a sub-set of forty-four psychiatrists in a 60-minute session to ensure consistency in SCIP administration and correction. The training phase was completed with a kappa index of agreement in scale correction and scoring of .99.

Data analysis

The analyses were done using the SPSS statistical package version 15 and the significance level was set at α = .01. The internal consistency of the SCIP was assessed by computing Cronbach’s alpha coefficient, treating each of the SCIP subtests as variables. We compared the SCIP scores of patients with schizophrenia and bipolar I disorder, as well as between males and females, using a t test for independent samples. In both cases, the statistical significance was supplemented by calculating Cohen’s d. Likewise, the differences between the specified groups were analyzed by age and educational level. The normal distribution of the data was tested using the Kolmogorov-Smirnov (KS) test for normality.

Patient performance on the SCIP was shown using various descriptive statistics (mean, standard deviation, median, asymmetry, kurtosis, and range of scores). As for the transformation of SCIP scores, a percentile, z-score, T-scores (T = 50 + 10 » z), and intelligence quotient (IQ = 100 + 15 » z) were calculated.

Results

Sample description

A total of 514 patients diagnosed according to DSM-IV-TR [41] criteria with schizophrenia (41.5%), schizoaffective disorder (6.4%), schizophreniform disorder (1.4%), or bipolar I disorder (50.7%) participated in this study. Within this group, 57.9% were males. Most patients with schizophrenia were being treated with a single antipsychotic (66.9%), although a large number were receiving a combination of two (28.0%) or three (3.1%) antipsychotics. At the time of assessment, 5 patients were not taking any antipsychotic. In addition to the antipsychotic medication, 52.6% of patients were receiving an additional psychoactive drug, primarily antidepressants and benzodiazepines. The mean age at onset of the illness was 24.25 (SD = 6.34), the mean number of months since the diagnosis was 156.78 (102.99), and the mean number of hospitalizations was 2.61 (3.05). Within the bipolar I disorder sample, 23.5% were taking lithium, while other patients were taking one (33.5%) or two (3.5%) antipsychotics in addition to lithium, and finally another group of patients were taking receiving antipsychotic medication in monotherapy (23.8%), or in a combination of two (5.0%), or three (0.4%) agents. Additionally, 75.4% of patients were receiving another type of psychoactive drug (i.e., antidepressants or benzodiazepines). The mean age at onset of the illness was 28.39 (8.34), the mean number of months since the diagnosis was 144.55 (95.78), the mean number of manic episodes they had experienced was 3.36 (1.86), and of depressive episodes was 1.22 (2.94), and finally the mean number of hospitalizations was 2.80 (3.67).

The participants were divided into two age groups (18–39 and 40–55) and two education level groups (less than secondary education and secondary education or higher). Based on these and other variables, Table 2 shows the main demographic information for each clinical sample used in the study.
Table 2

Sample descriptors

Variable (Percentage)

Schizophrenia (n = 254)

Bipolar I disorder (n = 260)

Sex

  

  Male

71.3

44.8

  Female

28.7

55.2

Educational level

  

  < Secondary education

33.9

33.5

  ≥ Secondary education

66.1

66.5

Age

  

  18 – 39

58.7

45.8

  40 – 55

41.3

54.2

Comparison between groups

By comparing the scores of patients with schizophrenia and bipolar disorder I, as well as between men and women, we obtained the means, standard deviations, t statistics, and effect sizes specified in Table 3.
Table 3

Mean SCIP scores and standard deviations by clinical diagnosis and sex of patients

 

Diagnosis

Sex

Subtest

Schizophrenia

Bipolar I

ttest

d

Male

Female

ttest

d

VLI-I

18.83 (4.07)

19.47 (4.10)

t(512) = 1.752

0.16

18.90 (3.97)

19.47 (4.22)

t(511) = 1.562

0.14

WMT

17.10 (4.46)

17.61 (4.20)

t(512) = 1.344

0.12

17.70 (4.26)

16.88 (4.40)

t(511) = 2.125

0.19

VFT

15.13 (5.74)

15.83 (5.83)

t(511) = 1.371

0.12

15.28 (5.55)

15.67 (5.98)

t(510) = 0.761

0.07

VLT-D

5.17 (2.35)

5.63 (2.34)

t(511) = 2.240

0.20

5.09 (2.38)

5.81 (2.24)

t(510) = 3.469*

0.31

PST

9.25 (3.54)

9.80 (3.49)

t(509) = 1.771

0.16

9.48 (3.58)

9.59 (3.46)

t(580) = 0.330

0.03

SCIP Total

65.50 (14.41)

68.20 (14.32)

t(507) = 2.120

0.19

66.39 (13.67)

67.37 (15.27)

t(506) = 0.755

0.07

* p < .01.

Both in the various subtests and in the total score, the mean performance of the patients with schizophrenia was poorer than that of the patients with bipolar I disorder, although in no case was the effect size measurement significant. With respect to sex, the mean scores were similar, with the exception of the VLT-D subtest, where women scored slightly better than men, although the corresponding effect size was small. As was to be expected, on all subtests, as well as on the total SCIP score, the patients with a higher education scored higher than those with a less than secondary education (all p values < .01), with effect sizes that varied between 0.50 for the VFT subtest and 0.70 for the PST subtest. The difference in total SCIP score, for the education variable, reached an effect size of 0.78. In the case of age, as the patients’ age increases, the mean scores decreased. The differences were statistically significant (p value < .01) for the total SCIP score and the various subtests, with the exception of the VLT-I and VFT. The effect sizes varied between 0.25 for the VLT-I subtest and 0.54 for the PST subtest, while the difference in total SCIP score was characterized by having an effect size of 0.53. For those reasons, the clinical normative benchmarks are presented jointly for male and female patients with schizophrenia and bipolar I disorder. On the other hand, given the existing differences, patient age and educational level were taken into account.

Standardization data

The internal consistency of the SCIP achieved a Cronbach’s alpha coefficient of 0.73, which is an acceptable value given the small number of variables. This alpha value did not increase when any of the component variables were eliminated. The item/scale correlations were between 0.44 for the VFT and 0.58 for the PST. The normal distribution of the data from the various subtests (and total SCIP-S score) was tested for each of the groups after combining the two age groups and the two participant educational level groups. In no case was the KS test statistically significant at a level of 0.01 (all p > .01) although in six cases there were p values below .05 (the WMT, VFT, VLT-D, and PST subtests in the 18–39 year old group and the VLT-D and PST subtests in the 40–55 year old group, in all cases with a secondary or higher education). Tables 4, 5, 6, 7 and 8 show the clinical normative data for each of the SCIP-S subtests in terms of percentiles, z-scores, T-scores, and IQ. Likewise, Table 9 shows this same information for the total SCIP-S score.
Table 4

Transformation of VLT-I subtest scores

< Secondary school

≥ Secondary school

18-39

40-55

18-39

40-55

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

0-11

< 3

< −1.72

< 33

< 74

0-9

1

< −2.19

< 28

< 67

0-5

1

< −3.45

< 16

< 48

0-9

1

< −2.4

< 26

< 64

0-11

< 3

< −1.72

< 33

< 74

0-9

1

< −2.19

< 28

< 67

6

1

−3.45

16

48

0-9

1

< −2.4

< 26

< 64

0-11

< 3

< −1.72

< 33

< 74

0-9

1

< −2.19

< 28

< 67

7

1

−3.21

18

52

0-9

1

< −2.4

< 26

< 64

0-11

< 3

< −1.72

< 33

< 74

9

1

−2.19

28

67

8

1

−2.96

20

56

0-9

1

< −2.4

< 26

< 64

0-11

< 3

< −1.72

< 33

< 74

10

2

−1.93

31

71

9

1

−2.72

23

59

10

1

−2.4

26

64

0-11

< 3

< −1.72

< 33

< 74

11

5

−1.67

33

75

10

1

−2.48

25

63

11

2

−2.14

29

68

12

3

−1.72

33

74

12

9

−1.41

36

79

11

3

−2.24

28

66

12

3

−1.89

31

72

13

9

−1.45

35

78

13

15

−1.15

39

83

12

4

−1.99

30

70

13

5

−1.63

34

76

14

13

−1.18

38

82

14

22

−0.89

41

87

13

5

−1.75

33

74

14

9

−1.37

36

79

15

19

−0.9

41

86

15

28

−0.62

44

91

14

8

−1.51

35

77

15

15

−1.12

39

83

16

30

−0.63

44

91

16

33

−0.36

46

95

15

10

−1.26

37

81

16

22

−0.86

41

87

17

43

−0.36

46

95

17

44

−0.10

49

98

16

14

−1.02

40

85

17

28

−0.61

44

91

18

51

−0.08

49

99

18

56

0.16

52

102

17

19

−0.78

42

88

18

36

−0.35

46

95

19

57

0.19

52

103

19

64

0.42

54

106

18

26

−0.54

45

92

19

46

−0.10

49

99

20

63

0.46

55

107

20

72

0.68

57

110

19

37

−0.29

47

96

20

54

0.16

52

102

21

70

0.73

57

111

21

81

0.94

59

114

20

47

−0.05

49

99

21

61

0.41

54

106

22

80

1.01

60

115

22

89

1.20

62

118

21

56

0.19

52

103

22

73

0.67

57

110

23

88

1.28

63

119

23

94

1.47

65

122

22

66

0.43

54

107

23

82

0.93

59

114

24

94

1.55

66

123

24

97

1.73

67

126

23

75

0.68

57

110

24

88

1.18

62

118

25

98

1.83

68

127

25

98

1.99

70

130

24

82

0.92

59

114

25

94

1.44

64

122

26-30

99

> 1.83

> 68

> 127

26

99

2.25

72

134

25

87

1.16

62

117

26

96

1.69

67

125

26-30

99

> 1.83

> 68

> 127

27

99

2.51

75

138

26

93

1.41

64

121

27

98

1.95

69

129

26-30

99

> 1.83

> 68

> 127

28-39

99

> 2.51

> 75

> 138

27

97

1.65

66

125

28

99

2.20

72

133

26-30

99

> 1.83

> 68

> 127

28-39

99

> 2.51

> 75

> 138

28

98

1.89

69

128

29

99

2.46

75

137

26-30

99

> 1.83

> 68

> 127

28-39

99

> 2.51

> 75

> 138

29

99

2.13

71

132

30

99

> 2.46

> 75

> 137

26-30

99

> 1.83

> 68

> 127

28-39

99

> 2.51

> 75

> 138

30

99

2.38

74

136

30

99

> 2.46

> 75

> 137

N

64

N

109

N

204

N

137

Mean

18.31

Mean

17.39

Mean

20.21

Mean

19.38

SD

3.660

SD

3.829

SD

4.120

SD

3.913

Median

18

Median

17

Median

20

Median

19

Skewness

0.048

Skewness

−0.045

Skewness

−0.378

Skewness

−0.125

Kurtosis

−1,023

Kurtosis

−0.456

Kurtosis

0.340

Kurtosis

−0.380

Range

12-25

Range

9-27

Range

6-30

Range

10-29

Table 5

Transformation of WMT subtest scores

< Secondary school

≥ Secondary school

18-39

40-55

18-39

40-55

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

0-1

1

< −3.25

< 17

< 51

0-4

1

< −2.40

< 26

< 64

0-5

1

< −3.27

< 17

< 51

0-3

1

< −3.19

< 18

< 52

2

1

−3.25

17

51

0-4

1

< −2.40

< 26

< 64

0-5

1

< −3.27

< 17

< 51

0-3

1

< −3.19

< 18

< 52

3

2

−3.04

20

54

0-4

1

< −2.40

< 26

< 64

0-5

1

< −3.27

< 17

< 51

0-3

1

< −3.19

< 18

< 52

4

2

−2.82

22

58

0-4

1

< −2.40

< 26

< 64

0-5

1

< −3.27

< 17

< 51

4

1

−3.19

18

52

5

2

−2.61

24

61

5

1

−2.40

26

64

0-5

1

< −3.27

< 17

< 51

5

1

−2.95

20

56

6

2

−2.39

26

64

6

1

−2.16

28

68

6

1

−3.27

17

51

6

1

−2.72

23

59

7

2

−2.18

28

67

7

1

−1.91

31

71

7

1

−3.01

20

55

7

2

−2.48

25

63

8

4

−1.97

30

71

8

3

−1.67

33

75

8

1

−2.75

22

59

8

3

−2.25

28

66

9

5

−1.75

32

74

9

8

−1.43

36

79

9

1

−2.49

25

63

9

3

−2.01

30

70

10

6

−1.54

35

77

10

13

−1.19

38

82

10

3

−2.23

28

67

10

4

−1.78

32

73

11

11

−1.32

37

80

11

18

−0.95

40

86

11

5

−1.97

30

70

11

7

−1.54

35

77

12

17

−1.11

39

83

12

27

−0.71

43

89

12

6

−1.71

33

74

12

10

−1.31

37

80

13

21

−0.89

41

87

13

35

−0.47

45

93

13

9

−1.45

36

78

13

14

−1.07

39

84

14

24

−0.68

43

90

14

43

−0.23

48

97

14

13

−1.19

38

82

14

19

−0.84

42

87

15

28

−0.46

45

93

15

52

0.01

50

100

15

18

−0.93

41

86

15

26

−0.60

44

91

16

34

−0.25

48

96

16

61

0.25

53

104

16

25

−0.67

43

90

16

35

−0.37

46

94

17

42

−0.03

50

99

17

67

0.49

55

107

17

32

−0.41

46

94

17

43

−0.13

49

98

18

48

0.18

52

103

18

71

0.73

57

111

18

39

−0.15

49

98

18

51

0.10

51

102

19

59

0.39

54

106

19

79

0.98

60

115

19

50

0.11

51

102

19

61

0.34

53

105

20

70

0.61

56

109

20

87

1.22

62

118

20

60

0.38

54

106

20

69

0.57

56

109

21

79

0.82

58

112

21

93

1.46

65

122

21

70

0.64

56

110

21

76

0.81

58

112

22

86

1.04

60

116

22

97

1.70

67

125

22

79

0.90

59

113

22

83

1.04

60

116

23

92

1.25

63

119

23

98

1.94

69

129

23

87

1.16

62

117

23

89

1.28

63

119

24

98

1.47

65

122

24

99

2.18

72

133

24

96

1.42

64

121

24

96

1.51

65

123

N

64

N

204

N

204

N

137

Mean

17.16

Mean

14.95

Mean

18.56

Mean

17.57

SD

4.661

SD

4.153

SD

3.838

SD

4.258

Median

18

Median

15

Median

19

Median

18

Skewness

−0.811

Skewness

0.020

Skewness

−0.625

Skewness

−0.549

Kurtosis

0.550

Kurtosis

−0.746

Kurtosis

−0.036

Kurtosis

0.171

Range

2-24

Range

5-24

Range

6-24

Range

4-24

Table 6

Transformation of VFT subtest scores

< Secondary school

≥ Secondary school

18-39

40-55

18-39

40-55

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

0-4

1

< −1.79

< 32

< 73

0-1

1

< −1.79

< 32

< 73

0-6

1

< −1.74

< 33

< 74

0-3

1

< −2.22

< 28

< 67

0-4

1

< −1.79

< 32

< 73

2

1

−1.79

32

73

0-6

1

< −1.74

< 33

< 74

0-3

1

< −2.22

< 28

< 67

0-4

1

< −1.79

< 32

< 73

3

2

−1.63

34

76

0-6

1

< −1.74

< 33

< 74

0-3

1

< −2.22

< 28

< 67

0-4

1

< −1.79

< 32

< 73

4

5

−1.46

35

78

0-6

1

< −1.74

< 33

< 74

4

1

−2.22

28

67

5

1

−1.79

32

73

5

7

−1.30

37

80

0-6

1

< −1.74

< 33

< 74

5

1

−2.04

30

69

6

2

−1.6

34

76

6

10

−1.14

39

83

0-6

1

< −1.74

< 33

< 74

6

1

−1.86

31

72

7

5

−1.42

36

79

7

16

−0.98

40

85

7

1

−1.74

33

74

7

2

−1.68

33

75

8

11

−1.23

38

82

8

22

−0.81

42

88

8

1

−1.55

34

77

8

5

−1.51

35

77

9

16

−1.05

40

84

9

29

−0.65

43

90

9

4

−1.37

36

79

9

7

−1.33

37

80

10

20

−0.86

41

87

10

35

−0.49

45

93

10

9

−1.18

38

82

10

9

−1.15

38

83

11

24

−0.68

43

90

11

40

−0.33

47

95

11

17

−1.00

40

85

11

13

−0.97

40

85

12

30

−0.49

45

93

12

44

−0.16

48

98

12

23

−0.81

42

88

12

19

−0.8

42

88

13

38

−0.31

47

95

13

51

0.00

50

100

13

29

−0.63

44

91

13

25

−0.62

44

91

14

48

−0.12

49

98

14

61

0.16

52

102

14

37

−0.44

46

93

14

33

−0.44

46

93

15

58

0.06

51

101

15

67

0.33

53

105

15

46

−0.26

47

96

15

42

−0.26

47

96

16

66

0.25

52

104

16

72

0.49

55

107

16

54

−0.07

49

99

16

50

−0.09

49

99

17

73

0.43

54

106

17

79

0.65

57

110

17

62

0.11

51

102

17

56

0.09

51

101

18

79

0.62

56

109

18

83

0.81

58

112

18

68

0.30

53

104

18

64

0.27

53

104

19

83

0.8

58

112

19

87

0.98

60

115

19

72

0.48

55

107

19

72

0.45

54

107

20

84

0.99

60

115

20

89

1.14

61

117

20

76

0.67

57

110

20

78

0.62

56

109

21

87

1.17

62

118

21

90

1.30

63

120

21

80

0.85

59

113

21

84

0.80

58

112

22

89

1.35

64

120

22

92

1.46

65

122

22

84

1.04

60

116

22

88

0.98

60

115

23

92

1.54

65

123

23

94

1.63

66

124

23

87

1.22

62

118

23

90

1.16

62

117

24

95

1.72

67

126

24

95

1.79

68

127

24

89

1.41

64

121

24

93

1.33

63

120

25

97

1.91

69

129

25

97

1.95

70

129

25

92

1.59

66

124

25

95

1.51

65

123

26

98

2.09

71

131

26

97

2.11

71

132

26

95

1.78

68

127

26

96

1.69

67

125

27

98

2.28

73

134

27

97

2.28

73

134

27

96

1.96

70

129

27

96

1.87

69

128

28

98

2.46

75

137

28

98

2.44

74

137

28

97

2.15

71

132

28

97

2.04

70

131

29

98

2.65

76

140

29

98

2.60

76

139

29

98

2.33

73

135

29

98

2.22

72

133

30

98

2.83

78

143

30

99

2.76

78

141

30

98

2.52

75

138

30

99

2.40

74

136

31

98

3.02

80

145

31

99

2.93

79

144

31

98

2.71

77

141

31

99

2.58

76

139

32

98

3.20

82

148

32

99

3.09

81

146

32

99

2.89

79

143

32

99

2.75

78

141

33

98

3.39

84

151

33

99

3.25

83

149

33

99

3.08

81

146

33

99

2.93

79

144

34

99

3.57

86

154

34

99

3.42

84

151

34

99

3.26

83

149

34

99

3.11

81

147

> 34

99

> 3.57

> 86

> 154

35

99

3.58

86

154

> 34

99

> 3.26

> 83

> 149

35

99

3.29

83

149

> 34

99

> 3.57

> 86

> 154

36

99

3.74

87

156

> 34

99

> 3.26

> 83

> 149

36

99

3.46

85

152

> 34

99

> 3.57

> 86

> 154

37

99

3.90

89

159

> 34

99

> 3.26

> 83

> 149

37

99

3.64

86

155

> 34

99

> 3.57

> 86

> 154

38

99

4.07

91

161

> 34

99

> 3.26

> 83

> 149

38

99

3.82

88

157

> 34

99

> 3.57

> 86

> 154

> 38

99

> 4.07

> 91

> 161

> 34

99

> 3.26

> 83

> 149

> 38

99

> 3.82

> 88

> 157

N

64

N

109

N

204

N

136

Mean

14.67

Mean

13.00

Mean

16.39

Mean

16.49

SD

5.410

SD

6.149

SD

5.401

SD

5.633

Median

14

Median

13

Median

15

Median

16

Skewness

0.828

Skewness

0.921

Skewness

0.785

Skewness

1.371

Kurtosis

1.488

Kurtosis

1.849

Kurtosis

0.374

Kurtosis

6.377

Range

5-34

Range

2-38

Range

7-34

Range

4-48

Table 7

Transformation of VLT-D subtest scores

< Secondary school

≥ Secondary school

18-39

40-55

18-39

40-55

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

0

2

−2.42

26

64

0

4

−1.94

31

71

0

1

−2.66

23

60

0

4

−1.94

31

71

1

4

−1.97

30

70

1

9

−1.50

35

77

1

1

−2.21

28

67

1

9

−1.5

35

77

2

7

−1.52

35

77

2

15

−1.07

39

84

2

4

−1.76

32

74

2

15

−1.07

39

84

3

14

−1.08

39

84

3

24

−0.63

44

91

3

10

−1.32

37

80

3

24

−0.63

44

91

4

24

−0.63

44

91

4

40

−0.20

48

97

4

20

−0.87

41

87

4

40

−0.2

48

97

5

41

−0.18

48

97

5

61

0.24

52

104

5

35

−0.42

46

94

5

61

0.24

52

104

6

62

0.26

53

104

6

76

0.68

57

110

6

50

0.02

50

100

6

76

0.68

57

110

7

77

0.71

57

111

7

85

1.11

61

117

7

66

0.47

55

107

7

85

1.11

61

117

8

88

1.16

62

117

8

93

1.55

65

123

8

80

0.92

59

114

8

93

1.55

65

123

9

94

1.60

66

124

9

97

1.98

70

130

9

90

1.36

64

120

9

97

1.98

70

130

10

98

2.05

71

131

10

99

2.42

74

136

10

97

1.81

68

127

10

99

2.42

74

136

N

64

N

109

N

204

N

136

Mean

5.41

Mean

4.45

Mean

5.95

Mean

5.34

SD

2.238

SD

2.295

SD

2.238

SD

2.401

Median

5

Median

4

Median

6

Median

6

Skewness

−0.153

Skewness

−0.025

Skewness

−0.156

Skewness

−0.410

Kurtosis

0.129

Kurtosis

−0.232

Kurtosis

−0.473

Kurtosis

−0.095

Range

0-10

Range

0-10

Range

0-10

Range

0-10

Table 8

Transformation of PST subtest scores

< Secondary school

≥ Secondary school

18-39

40-55

18-39

40-55

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

0-1

1

< −2.47

< 25

< 63

0

1

−2.16

28

68

0-2

1

< −2.55

< 25

<62

0-2

1

< −1.81

< 32

< 73

0-1

1

< −2.47

< 25

< 63

1

1

−1.86

31

72

0-2

1

< −2.55

< 25

<62

0-2

1

< −1.81

< 32

< 73

2

1

−2.47

25

63

2

3

−1.57

34

76

0-2

1

< −2.55

< 25

<62

0-2

1

< −1.81

< 32

< 73

3

2

−2.12

29

68

3

10

−1.27

37

81

3

1

−2.55

25

62

3

1

−1.81

32

73

4

3

−1.77

32

73

4

20

−0.98

40

85

4

2

−2.20

28

67

4

2

−1.56

34

77

5

9

−1.42

36

79

5

29

−0.69

43

90

5

4

−1.86

31

72

5

6

−1.3

37

80

6

16

−1.07

39

84

6

38

−0.39

46

94

6

8

−1.52

35

77

6

13

−1.05

40

84

7

25

−0.72

43

89

7

49

−0.10

49

99

7

14

−1.18

38

82

7

19

−0.79

42

88

8

38

−0.38

46

94

8

58

0.20

52

103

8

20

−0.84

42

87

8

29

−0.53

45

92

9

48

−0.03

50

100

9

67

0.49

55

107

9

27

−0.50

45

92

9

40

−0.28

47

96

10

59

0.32

53

105

10

78

0.79

58

112

10

41

−0.16

48

98

10

54

−0.02

50

100

11

71

0.67

57

110

11

84

1.08

61

116

11

56

0.18

52

103

11

65

0.24

52

104

12

84

1.02

60

115

12

89

1.37

64

121

12

70

0.52

55

108

12

75

0.49

55

107

13

93

1.37

64

120

13

95

1.67

67

125

13

81

0.86

59

113

13

84

0.75

57

111

14

95

1.71

67

126

14

98

1.96

70

129

14

88

1.20

62

118

14

89

1.00

60

115

15

98

2.06

71

131

15

98

2.26

73

134

15

94

1.54

65

123

15

93

1.26

63

119

16-30

> 98

> 2.06

> 71

> 131

16

99

2.55

76

138

16

97

1.88

69

128

16

95

1.52

65

123

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

17

99

2.22

72

133

17

96

1.77

68

127

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

18

97

2.03

70

130

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

19

97

2.29

73

134

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

20

98

2.54

75

138

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

21

99

2.80

78

142

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

22

99

3.05

81

146

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

23

99

3.31

83

150

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

24

99

3.57

86

154

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

25

99

3.82

88

157

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

26

99

4.08

91

161

16-30

> 98

> 2.06

> 71

> 131

17-30

99

> 2.55

> 76

> 136

18-30

99

> 2.22

> 72

> 133

27-30

99

> 4.08

> 91

> 161

N

64

N

109

N

202

N

136

Mean

9.08

Mean

7.33

Mean

10.48

Mean

10.08

SD

2.869

SD

3.399

SD

2.939

SD

3.920

Median

9

Median

7

Median

11

Median

10

Skewness

−0.093

Skewness

0.275

Skewness

−0.194

Skewness

1.694

Kurtosis

−0.458

Kurtosis

−0.522

Kurtosis

−0.107

Kurtosis

6.269

Range

2-15

Range

0-16

Range

3-17

Range

3-30

Table 9

Transformation of total scale scores

< Secondary school

≥ Secondary school

18-39

40-55

18-39

40-55

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

PD

P

z

T

IQ

0-35

1

< −2.18

< 28

< 67

0-20

1

< −2.26

< 27

< 66

0-20

1

< −3.47

< 15

< 48

0-35

1

< −2.28

< 27

< 66

0-35

1

< −2.18

< 28

< 67

21-25

1

−2.26

27

66

21-25

1

< −3.47

< 15

< 48

0-35

1

< −2.28

< 27

< 66

0-35

1

< −2.18

< 28

< 67

26-30

2

−1.93

31

71

26-30

1

−3.47

15

48

0-35

1

< −2.28

< 27

< 66

0-35

1

< −2.18

< 28

< 67

31-35

6

−1.60

34

76

31-35

1

−3.072

19

54

0-35

1

< −2.28

< 27

< 66

36-40

1

−2.18

28

67

36-40

10

−1.27

37

81

36-40

1

−2.673

23

60

36-40

1

−2.28

27

66

41-45

2

−1.77

32

73

41-45

17

−0.93

41

86

41-45

1

−2.274

27

66

41-45

3

−1.91

31

71

46-50

7

−1.36

36

80

46-50

26

−0.60

44

91

46-50

2

−1.876

31

72

46-50

6

−1.54

35

77

51-55

19

−0.95

40

86

51-55

39

−0.27

47

96

51-55

6

−1.477

35

78

51-55

12

−1.17

38

82

56-60

33

−0.54

45

92

56-60

54

0.06

51

101

56-60

14

−1.079

39

84

56-60

21

−0.80

42

88

61-65

45

−0.13

49

98

61-65

64

0.39

54

106

61-65

25

−0.68

43

90

61-65

32

−0.43

46

94

66-70

56

0.28

53

104

66-70

76

0.72

57

111

66-70

38

−0.28

47

96

66-70

44

−0.06

49

99

71-75

67

0.69

57

110

71-75

85

1.05

61

116

71-75

53

0.12

51

102

71-75

60

0.31

53

105

76-80

82

1.09

61

116

76-80

91

1.38

64

121

76-80

66

0.52

55

108

76-80

75

0.68

57

110

81-85

93

1.50

65

123

81-85

95

1.71

67

126

81-85

79

0.91

59

114

81-85

84

1.05

60

116

86-90

99

1.91

69

129

86-90

96

2.04

70

131

86-90

89

1.31

63

120

86-90

91

1.42

64

121

> 90

99

> 1.91

> 69

> 129

91-95

98

2.37

74

136

91-95

95

1.71

67

126

91-95

95

1.79

68

127

> 90

99

> 1.91

> 69

> 129

> 96

> 98

> 2.37

> 74

> 136

96-100

99

2.11

71

132

96-100

98

2.16

72

132

> 90

99

> 1.91

> 69

> 129

> 96

> 96

> 96

> 96

> 96

> 101

99

> 2.11

> 71

> 132

101-105

99

2.53

75

138

> 90

99

> 1.91

> 69

> 129

> 96

> 96

> 96

> 96

> 96

> 101

99

> 2.11

> 71

> 132

> 105

99

> 2.53

> 75

> 138

N

64

N

109

N

202

N

134

Mean

64.63

Mean

57.12

Mean

71.53

Mean

68.84

SD

12.213

SD

15.109

SD

12.544

SD

13.507

Median

64

Median

56

Median

71

Median

70

Skewness

−0.007

Skewness

0.156

Skewness

−0.123

Skewness

−0.034

Kurtosis

−0.890

Kurtosis

−0.124

Kurtosis

−0.218

Kurtosis

−0.244

Range

37-88

Range

21-94

Range

30-98

Range

38-105

After administering the instrument, for norming the cognitive performance of a patient with schizophrenia or bipolar I disorder against the reference or comparator group, the examiner has only to locate the corresponding transformed score on the table by the patient’s age and educational level.

Discussion

The clinical value of a screening tool is directly related to either considering cognitive impairment a key aspect of schizophrenic psychopathology and, according to the proposed DSM-V revisions, recommending it as one key dimension to be measured in all patients with a psychotic disorder, or including cognitive deficit as one of the diagnostic criteria for psychoses as suggested by some authors [46, 47]. The practical utility of the administered tests should not be forgotten when conducting a neuropsychological assessment, and since there is a large number of psychiatric patients (accounting for around 2% of the general population) who require diagnosis, there is a growing need for cost-effective and highly efficient diagnostic tools. In this regard, the creation of the SCIP-S precisely had these two objectives. Previous studies [33, 34] have shown that the SCIP-S takes approximately 15 minutes to administer, compared to a mean of around 75 minutes for the administration of a full neuropsychological battery or between 60–90 minutes for the MCCB, and it has good validity and reliability. Furthermore, Rojo et al. [35] reported the good sensitivity and specificity of the test and its high diagnostic value for appropriately distinguishing cognitively preserved from cognitively impaired individuals.

This study goes a step farther and presents the diagnostic-specific norms and performance for the SCIP-S according to the age and educational level of subjects in a large sample of patients with schizophrenia and bipolar disorder. Some studies report differences in neuropsychological performance between subjects with different educational levels [48, 49], and such differences were also found in this study, which is why in the SCIP-S standardization data the sample has been divided based on educational level. It should be pointed out that, although there is not always a direct correspondence between educational level and years of education, we may consider that in the vast majority of cases a less than secondary education implies fewer than 12 years of education, while a secondary or higher educational level implies at least 12 years of education.

Another aspect known to influence cognitive performance is age, as over the years a series of cortical changes occurs resulting in a loss of brain volume [50, 51] associated with a decrease in cognitive performance in the general population [52, 53]. Our norms take this aspect into account by dividing the sample according to age and limiting patient age to 55 years in order not to introduce bias due to patients whose performance could be affected by early onset of a picture of dementia.

One item that bears emphasizing refers to that fact that a certain pattern was observed to repeat in the various subtests and total SCIP-S score. Specifically, the median score in the two age groups is similar (a maximum difference of 1 point) when the patients have a high educational level, while differences of up to 8 points are found in the groups with a primary or lower educational level. This may be explained by an interaction between age and educational level and the possibility that age at onset of illness plays an important role, since some studies show that both verbal intelligence and impairment of verbal memory and executive functioning could be affected in patients before they experience their first psychotic episode [5456], such that the earlier the onset of illness, the greater the potential for limiting the patient’s ability to normally pursue an education. Therefore, the effects of age and educational level and their interaction were explored by adding age at onset of illness as a covariate. Such interaction was not statistically significant in any SCIP-S score (all p > .01).

One of the aspects highlighted by this study is that of all the tests mentioned above that have been developed for the purposes of cognitively assessing psychiatric patients, we find diagnosis-specific standardization data for patients with schizophrenia only for the RBANS [39, 40]. And as stated by Iverson et al. [40], being able to describe a patient’s cognitive performance in terms of expectation for their peer group is more useful to multidisciplinary treatment teams than just comparing them to a healthy population. So the present study provides the tools necessary to interpret the score obtained by a patient with functional psychosis relative to other patients after administration of the SCIP-S scale. As an example, let us apply the SCIP-S to an imaginary 38 year old college graduate diagnosed with schizophrenia who obtains a direct score of 13 on the WMT subtest. After determining their performance relative to healthy controls (healthy control norms are under elaboration), the clinician interest could move to answer the question: Where do we situate that individual with respect to other patients? Looking at Table 4, we see that, based on his age and educational level, this patient is in the 9th percentile, has a z-score of −1.45, a T-score of 36, and an IQ of 78. This tells us that only 9% of his comparison group has obtained a score below his and that his working memory score of 13 is situated approximately 1.5 standard deviations below the other patients.

Conclusions

The SCIP and the SCIP-S provides a quick and convenient cognitive diagnosis and, in that regard, its usefulness extends to areas such as detection, cognitive assessment of large samples, epidemiological and screening diagnostic studies more than to specific cognitive domains or type of impairment in patients with functional psychosis. In that way, it is a complementary test that is not intended to replace complete neuropsychological batteries. Future studies should explore how performance on the SCIP relates to other cognitive domains that it does not measure directly (e.g., problem solving, social cognition, etc.).

A study that could continue this one should be perform standardization data for patients over 55 years of age, since, at a cognitive level, that is a critical age where the SCIP-S could help us reach a differential diagnosis between onset of dementia versus cognitive dysfunction associated with functional psychosis. Future research with this scale should also incorporate the development of guidelines for interpreting the scoring according to results of treatment of patients. Additionally, last evidences in schizophrenic and bipolar patients from recent studies have suggested that the history of psychosis explain part of the neurocognitive performance [57], thus in future studies with cognitive screening tools would be interesting to take this variable into account.

In short, this study presents the first jointly developed diagnostic-specific norms for the SCIP for functional psychosis (schizophrenia and bipolar disorder), providing increased information about their cognitive abilities.

Declarations

Acknowledgements

Authors sincerely thanks Pfizer Spain for funding the study. Also, authors wish to thank participant psychiatrists for kindly collect data for analysis of this study.

Role of funding source

Data collection and analysis were funded by Pfizer Spain. Main analysis was conducted at the Universidad of Barcelona which received a grant from Pfizer Spain to carry out the statistical analysis. The funding body has no role in analysis of data. All authors had complete access to the data, participated in the analysis and/or interpretation of results, and drafted the manuscript.

Authors’ Affiliations

(1)
Department of Methodology, Faculty of Psychology, University of Barcelona
(2)
Institute for Brain, Cognition, and Behavior (IR3C), University of Barcelona
(3)
Department of Psychiatry, Benito Menni CASM, Granollers Hospital General
(4)
Department of Medicine, Teaching Unit of Psychiatry and Psychological Medicine, University of Valencia, CIBERSAM
(5)
Schizophrenia Program Clinic, Institute of Neuroscience, Hospital Clinic i Provincial, IDIBAPS, University of Barcelona, CIBERSAM
(6)
Bipolar Disorders Program, Institute of Neuroscience, Hospital Clinic i Provincial, IDIBAPS, CIBERSAM, University of Barcelona
(7)
Department of Psychiatry, Hospital Provincial Rodríguez Chamorro
(8)
Psychiatric Hospitalization Unit, Hospital Virgen del Camino
(9)
Department of Psychiatry, Hospital University Marqués de Valdecilla
(10)
Department of Psychiatry, Bebensee Schizophrenia Research Unit, University of Alberta
(11)
Department of Neurosciences, Medical Unit, Pfizer Spain
(12)
Health Outcomes Research Department, Medical Unit, Pfizer Spain

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  58. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1471-244X/13/127/prepub

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© Gómez-Benito et al.; licensee BioMed Central Ltd. 2013

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.