The Gordon Diagnostic System and WISC–III Freedom from Distractibility Index: Validity in Identifying Clinic-Referred Children with and without ADHD

2002 ◽  
Vol 91 (2) ◽  
pp. 575-587 ◽  
Author(s):  
Susan Dickerson Mayes ◽  
Susan L. Calhoun
Keyword(s):  
Diagnostic Accuracy ◽  
Predictive Power ◽  
Diagnostic System ◽  
Composite Score ◽  
Diagnostic Agreement ◽  
Gordon Diagnostic System ◽  
Two Measures ◽  
Freedom From Distractibility ◽  
New Criteria

Gordon Diagnostic System and WISC–III scores for clinic-referred 6-to 16-yr.-olds (184 with ADHD Combined Type and 46 without ADHD) were analyzed to evaluate the combination of scores and cutpoints that maximized diagnostic accuracy. Using an “ADHD cutpoint” of IQ minus the GDS Composite score ≥ 13, 87.8% of the children were correctly identified as having or not having ADHD. IQ minus Freedom from Distractibility >0 yielded 73.5% accuracy. When the two measures were combined to create new criteria (IQ minus GDS Composite ≥ 13 or IQ minus Freedom from Distractibility ≥ 11), diagnostic accuracy increased to 90.9% and negative predictive power improved substantially Diagnostic agreement between the Gordon Diagnostic System and Freedom from Distractibility was 70%, suggesting that the two tests measure both similar and unique traits.

scholarly journals A Novel Fault Diagnosis System on Polymer Insulation of Power Transformers Based on 3-stage GA–SA–SVM OFC Selection and ABC–SVM Classifier

Polymers ◽  
2018 ◽  
Vol 10 (10) ◽  
pp. 1096 ◽  
Author(s):  
Xiaoge Huang ◽  
Yiyi Zhang ◽  
Jiefeng Liu ◽  
Hanbo Zheng ◽  
Ke Wang
Keyword(s):  
Artificial Intelligence ◽  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Diagnostic System ◽  
Gas Analysis ◽  
Diagnostic Methods ◽  
Power Transformers ◽  
Svm Classifier ◽  
Diagnosis System ◽  
Optimal Feature

Dissolved gas analysis (DGA) has been widely used in various scenarios of power transformers’ online monitoring and diagnoses. However, the diagnostic accuracy of traditional DGA methods still leaves much room for improvement. In this context, numerous new DGA diagnostic models that combine artificial intelligence with traditional methods have emerged. In this paper, a new DGA artificial intelligent diagnostic system is proposed. There are two modules that make up the diagnosis system. The two modules are the optimal feature combination (OFC) selection module based on 3-stage GA–SA–SVM and the ABC–SVM fault diagnosis module. The diagnosis system has been completely realized and embodied in its outstanding performances in diagnostic accuracy, reliability, and efficiency. Comparing the result with other artificial intelligence diagnostic methods, the new diagnostic system proposed in this paper performed superiorly.

scholarly journals How to Measure Financial Literacy?

2020 ◽  
Vol 13 (12) ◽  
pp. 324
Author(s):  
Marc Oliver Rieger
Keyword(s):  
Financial Literacy ◽  
Predictive Power ◽  
Investment Decisions ◽  
Measurement Instruments ◽  
Stock Investment ◽  
Potential Alternative ◽  
Two Measures

Financial (il-)literacy and its effects have been studied extensively in recent years. The measurement of this concept is, however, tricky and numerous measurement instruments exist. In this paper, we study the connection between these measures empirically. We find that these measures are often only slightly related and that this is a so-far overlooked empirical problem in this field. As a result of our analysis, we suggest the combination of two measures as the best potential alternative to the existing measures. Finally, we analyze the predictive power of this suggested measure for stock investment decisions.

scholarly journals Risk assessment criteria in liver transplantation for hepatocellular carcinoma: proposal to improve transplant oncology

2020 ◽  
Vol 7 (3) ◽  
pp. HEP26
Author(s):  
John C McVey ◽  
Kazunari Sasaki ◽  
Daniel J Firl
Keyword(s):  
Hepatocellular Carcinoma ◽  
Liver Transplantation ◽  
Clinical Practice ◽  
Predictive Power ◽  
Liver Allocation ◽  
Recent Developments ◽  
The Right ◽  
Practice Patient ◽  
New Criteria ◽  
Key Questions

Liver transplantation for hepatocellular carcinoma has proved to be a highly effective cure if the right patient can be selected. Milan criteria has traditionally guided physicians toward appropriate liver allocation but changes in clinical practice, patient populations and recent developments in biomarkers are decreasing Milan criteria’s utility. At the same time, the literature has flooded with a diversity of new criteria that demonstrate strong predictive power and are better suited for current clinical practice. In this article, the utility of newly proposed criteria will be reviewed and important issues to improve future criteria will be addressed in hopes of opening a discussion on how key questions surrounding criteria for liver transplantation of hepatocellular carcinoma can be answered.

scholarly journals Seeking a Valid Gold Standard for an Innovative, Dialect-Neutral Language Test

2014 ◽  
Vol 57 (2) ◽  
pp. 495-508 ◽  
Author(s):  
Barbara Zurer Pearson ◽  
Janice E. Jackson ◽  
Haotian Wu
Keyword(s):  
Diagnostic Accuracy ◽  
Language Impairment ◽  
Gold Standard ◽  
Predictive Power ◽  
Clinical Status ◽  
Variation Norm ◽  
American Children ◽  
Sensitivity Specificity ◽  
Neutral Language ◽  
Gold Standards

PurposeIn this study, the authors explored alternative gold standards to validate an innovative, dialect-neutral language assessment.MethodParticipants were 78 African American children, ages 5;0 (years;months) to 6;11. Twenty participants had previously been identified as having language impairment. The Diagnostic Evaluation of Language Variation—Norm Referenced (DELV–NR; Seymour, Roeper, & J. de Villiers, 2005) was administered, and concurrent language samples (LSs) were collected. Using LS profiles as the gold standard, sensitivity, specificity, and other measures of diagnostic accuracy were compared for diagnoses made from the DELV–NR and participants' clinical status prior to recruitment. In a second analysis, the authors used results from the first analysis to make evidence-based adjustments in the estimates of DELV–NR diagnostic accuracy.ResultsAccuracy of the DELV–NR relative to LS profiles was greater than that of prior diagnoses, indicating that the DELV–NR was an improvement over preexisting diagnoses for this group. Specificity met conventional standards, but sensitivity was somewhat low. Reanalysis using the positive and negative predictive power of the preexisting diagnosis in a discrepant-resolution procedure revealed that estimates for sensitivity and specificity for the DELV–NR were .85 and .93, respectively.ConclusionThe authors found that, even after making allowances for the imperfection of available gold standards, clinical decisions made with the DELV–NR achieved high values on conventional measures of diagnostic accuracy.

An Information Fusion Model Based on Dempster–Shafer Evidence Theory for Equipment Diagnosis

2017 ◽  
Vol 4 (2) ◽  
Author(s):  
Dengji Zhou ◽  
Tingting Wei ◽  
Huisheng Zhang ◽  
Shixi Ma ◽  
Fang Wei
Keyword(s):  
Fault Diagnosis ◽  
Diagnostic Accuracy ◽  
Information Fusion ◽  
Evidence Theory ◽  
Diagnostic System ◽  
Fusion Model ◽  
First Case ◽  
Evidence Algorithm ◽  
Data Layer ◽  
Analytical System

An abnormal operating effect can be caused by different faults, and a fault can cause different abnormal effects. An information fusion model, with hybrid-type fusion frame, is built in this paper, so as to solve this problem. This model consists of data layer, feature layer and decision layer, based on an improved Dempster–Shafer (D-S) evidence algorithm. After the data preprocessing based on event reasoning in data layer and feature layer, the information will be fused based on the new algorithm in decision layer. Application of this information fusion model in fault diagnosis is beneficial in two aspects, diagnostic applicability and diagnostic accuracy. Additionally, this model can overcome the uncertainty of information and equipment to increase diagnostic accuracy. Two case studies are implemented by this information fusion model to evaluate it. In the first case, fault probabilities calculated by different methods are adopted as inputs to diagnose a fault, which is quite different to be detected based on the information from a single analytical system. The second case is about sensor fault diagnosis. Fault signals are planted into the measured parameters for the diagnostic system, to test the ability to consider the uncertainty of measured parameters. The case study result shows that the model can identify the fault more effectively and accurately. Meanwhile, it has good expansibility, which may be used in more fields.

Measurements of attention deficits and impulsivity: a Swedish study of the Gordon Diagnostic System

2007 ◽  
Vol 88 (11) ◽  
pp. 1262-1268 ◽  
Author(s):  
E El-Sayed ◽  
I van't Hooft ◽  
I Fried ◽  
J-O Larsson ◽  
K Malmberg ◽  
...  
Keyword(s):  
Diagnostic System ◽  
Attention Deficits ◽  
Swedish Study ◽  
Gordon Diagnostic System

scholarly journals Using the Draw-a-Story Drawing Test to Predict Perceived Stress, Military Life Adjustment, and Resilience

2021 ◽  
Vol 13 (13) ◽  
pp. 7383
Author(s):  
Juliet Jue ◽  
Jung-Hee Ha
Keyword(s):  
Perceived Stress ◽  
Predictive Power ◽  
Mental States ◽  
Emotional Content ◽  
Self Image ◽  
Drawing Test ◽  
Military Life ◽  
Resilience Scale ◽  
Two Measures ◽  
The Military

In this study, we investigated how effectively a Draw-a-Story drawing test can predict the perceived stress, military life adjustment, and resilience of soldiers. A total of 276 conscripted male soldiers participated in the study. The research tools included the Perceived Stress Scale, the Military Life Adjustment Scale, the Connor–Davidson Resilience Scale (Korean version), and the Draw-a-Story drawing test. The results of the correlation and regression analyses were as follows. First, perceived stress, military life adjustment, resilience, and DAS emotional content were all mutually correlated. The DAS self-image was positively correlated only with resilience. Second, emotional content predicted soldiers’ perceived stress, military life adjustment, and resilience at significant levels. Meanwhile, our regression analysis showed that self-image did not have significant predictive power. In this paper, we discuss the implications for predictive power of our findings regarding the two measures of DAS; we also propose that DAS could serve as a tool to predict the mental states of soldiers.

Combining Cognitive and Personality Measures of Impulse Control in the Assessment of Childhood ADHD

2012 ◽  
Vol 28 (3) ◽  
pp. 208-215 ◽  
Author(s):  
Wolfgang A. Rauch ◽  
Andreas Gold ◽  
Kathrin Schmitt
Keyword(s):  
Diagnostic Accuracy ◽  
Impulse Control ◽  
Theoretical Models ◽  
Parent Ratings ◽  
Personality Questionnaire ◽  
Cognitive Measures ◽  
Unique Variance ◽  
Two Measures ◽  
Personality Questionnaires ◽  
Adhd Symptomatology

Impulse control is measured both with personality questionnaires and in cognitive laboratory tasks, yet previous results concerning the convergence of these two types of instruments are inconsistent. The current study measured impulse control of children with and without ADHD with a personality questionnaire and a go/no-go task as well as with parent ratings of ADHD symptomatology. Scores on the two measures correlate weakly with one another, yet both correlate moderately to strongly with parent ratings – and both explain unique variance of parent ratings. Accordingly, the simple sum of the standardized scores from the two measures outperforms the single measures in diagnostic accuracy and association with ADHD symptomatology. Results show that a conjoint application of personality and cognitive measures of impulse control is useful for an extended assessment of ADHD. The adequacy of personality and cognitive measures for assessing distinct facets of impulse control is discussed with regard to theoretical models of impulsivity and with regard to ADHD symptomatology.

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