Diagnostic Interviews

Carlisle, L. Lee; McClellan, Jon

doi:10.1176/appi.books.9781585623921.md07

Cited by 3 publications

(6 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The KIDDIE-SADS is a semistructured diagnostic interview 61 widely used in child psychiatry. 40,62 High levels of interrater agreement (93%-100%) and test-retest reliability (κ: 0.63-0.90) were found for the most frequent disorders (present time) in youth aged 7 to 17 years. The criterion-related validity was verified by using various external criteria.…”

Section: Methodsmentioning

confidence: 96%

“…43,44 Various structured or semistructured diagnostic interviews were designed to assess a large number of DSM diagnoses according to adolescent report. 40,42 However, as we elaborated elsewhere, 43,44 their long administration time (60-120 minutes) and high level of complexity, particularly for adolescents aged 12 to 13 years, preclude their use by frontline service providers. From a prevention perspective, the development of an age-appropriate self-report screening instrument assessing DSM-5 symptoms for disorders, 43,44 borderline personality traits, 31 and suicidality, 18,37 in the continuum of youth mental health services, remains a major challenge for clinicians and researchers.…”

mentioning

confidence: 95%

“…Since no single informant can be considered the “gold standard,” 39 –42 it is relevant to include an adolescent’s DSM -based self-report to provide unique, hence useful, information in a multi-informant evaluation process of youth’s psychopathology. 43,44 Various structured or semistructured diagnostic interviews were designed to assess a large number of DSM diagnoses according to adolescent report.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Reliability, Validity, and Clinical Utility of the Dominic Interactive for Adolescents–Revised

et al. 2016

View full text Add to dashboard Cite

Objectives: The Dominic Interactive for Adolescents-Revised (DIA-R) is a multimedia self-report screen for 9 mental disorders, borderline personality traits, and suicidality defined by the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). This study aimed to examine the reliability and the validity of this instrument.Methods: French-and English-speaking adolescents aged 12 to 15 years (N ¼ 447) were recruited from schools and clinical settings in Montreal and were evaluated twice. The internal consistency was estimated by Cronbach alpha coefficients and the test-retest reliability by intraclass correlation coefficients. Cutoff points on the DIA-R scales were determined by using clinically relevant measures for defining external validation criteria: the Schedule for Affective Disorders and Schizophrenia for School-Aged Children, the Beck Hopelessness Scale, and the Abbreviated-Diagnostic Interview for Borderlines. Receiver operating characteristic (ROC) analyses provided accuracy estimates (area under the ROC curve, sensitivity, specificity, likelihood ratio) to evaluate the ability of the DIA-R scales to predict external criteria. Results: For most of the DIA-R scales, reliability coefficients were excellent or moderate. High or moderate accuracy estimates from ROC analyses demonstrated the ability of the DIA-R thresholds to predict psychopathological conditions. These thresholds were generally capable to discriminate between clinical and school subsamples. However, the validity of the obsessions/compulsions scale was too low.

show abstract

Section: Methodsmentioning

confidence: 96%

mentioning

confidence: 95%

mentioning

confidence: 99%

See 1 more Smart Citation

Reliability, Validity, and Clinical Utility of the Dominic Interactive for Adolescents–Revised

et al. 2016

View full text Add to dashboard Cite

show abstract

“…Finally, it is also important to consider that while the criterion standards employed throughout this review (i.e. diagnostic interview schedules based on DSM or ICD-10 diagnostic criteria such as the K-SADs) are currently the gold standard in detecting the presence of a depressive disorder (Carlisle and McClellan, 2009), they are not perfect measures, and are also open to the same issues of reliability and validity as the screening scales themselves (Hodges, 1993).…”

Section: Limitationsmentioning

confidence: 99%

Symptom screening scales for detecting major depressive disorder in children and adolescents: A systematic review and meta-analysis of reliability, validity and diagnostic utility

Stockings

Degenhardt

Lee

et al. 2015

Journal of Affective Disorders

218

162

View full text Add to dashboard Cite

show abstract

“…To reduce inter-rater variability multiple interviews can be conducted by different psychiatrists. A consensus interview between two or more psychiatrists according to standardised diagnostic criteria is currently considered the gold standard [45]. In this context, I now review the main structured diagnostic tools.…”

Section: Consensus Interviewsmentioning

confidence: 99%

Biometric markers for affective disorders

Lord¹

View full text Add to dashboard Cite

Effective treatment of affective disorders is often challenged by incorrect or delayed diagnosis. Patients with bipolar disorder (BD) typically receive between 1 and 4 prior diagnoses of other mental health disorders preceding correct diagnosis, commonly with a delay from illness onset of 10 years, highlighting the difficulty of accurate diagnosis in a clinical setting. Most commonly, bipolar disorder is misdiagnosed as unipolar major depressive disorder (MDD), with between 25 and 50% of people diagnosed with depression being later re-classified as bipolar. The current problem of misdiagnosis is compounded by the lack of laboratory based quantitative testing for affective disorders available in clinical situations.The overarching objectives of this thesis are to demonstrate the utility of machine learning algorithms to neuroimaging data in order to provide a computational platform for classification, diagnosis and prediction of mood disorders. The thesis hence aims to unify three main themes -namely the clinical psychiatry of affective (mood) disorders, analytic principles of neuroimaging data (such as networkbased platforms) and machine learning.I first combine network theory and machine learning to address the issue of diagnostic classification using resting state functional magnetic resonance imaging (fMRI). I find that key metrics describing functional organization of the brain can be harnessed to yield accurate diagnostic boundaries in the data set I examine. In particular, the organization of ongoing brain activity into communities provides key information which distinguishes MDD from healthy controls.I next investigate a core group of brain regions which modulate network efficiency at a global scale, known as the rich club. Although this has previously been examined in structural brain networks, I apply it to resting state functional images. Furthermore, I investigate the robustness of the functional rich club following the correction of systematic artifacts in fMRI scans. Here I conclude a densely connected rich club is present in resting state fMRI across using all processing styles. However, the composition of members changes depending on key choices in the preprocessing of data. Further validation of the rich club should assess the impact of using different scanners or brain atlases.From chapter 4 onward, I shift the focus from depression to bipolar disorder. Here I start by assessing the functional connectivity of the inferior frontal gyrus, a region which is down regulated during an ii emotional recognition task in first degree relatives of bipolar subjects. Using a technique capable of identifying subtle alterations in functional networks I identified a community of connected brain regions with substantially reduced functional connectivity in bipolar subjects. I used machine learning to distinguish people with bipolar from first degree relatives and a healthy cohort. This was most effective between bipolar subjects and control subjects, while identifying the first degree relatives from eith...

show abstract

Diagnostic Interviews

Cited by 3 publications

References 0 publications

Reliability, Validity, and Clinical Utility of the Dominic Interactive for Adolescents–Revised

Reliability, Validity, and Clinical Utility of the Dominic Interactive for Adolescents–Revised

Symptom screening scales for detecting major depressive disorder in children and adolescents: A systematic review and meta-analysis of reliability, validity and diagnostic utility

Biometric markers for affective disorders

Contact Info

Product

Resources

About