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Background: The current coronavirus disease-19 (COVID-19) pandemic call attention to the key role informatics play in healthcare. The present study discovers an independent role of computerised tomography chest (CT) scans in prognosis of COVID-19 using classification learning algorithms. Methods: In this retrospective study, 57 RT PCR positive COVID-19 patients were enrolled from SMS Medical College, Jaipur (Rajasthan, India) after approval from the Institutional Ethics Committee. A set of 21 features including clinical findings and laboratory parameters and chest CT severity score were recorded. The CT score with mild, moderate and severe categories was chosen as response variable. The dimensionality reduction of feature space was performed and classifiers including, decision trees, K-nearest neighbours, support vector machine and ensemble learning were trained with principal components. The model with highest accuracy and area under the ROC curve (AUC) was selected. Results: The median age of patients was 55 years (range: 20-99 years) with 37 males. The feature space was reduced from 21 to 7 predictors, that included fever, cough, fibrin degradation products, haemoglobin, neutrophil-lymphocyte ratio, ferritin and procalcitonin. The linear support vector machine was chosen as the best classifier with 73.7 % and 0.69 accuracy and AUC for severe CT chest score, respectively. The variance contributed by first three principal components were 97.5 %, 2.4 % and 0.0 %, respectively. Conclusion: In view of low degree of relationships between predictors and chest CT scan severity score category as interpreted from accuracy and AUC it can be concluded that chest CT scan has an independent role in the prognosis of COVID-19 patients.

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Objective: Behavioral rating scales are widely used assessment measures for attention-deficit/hyperactivity disorder (ADHD; Handler & DuPaul, 2005).The objective of this review was to examine the ecological validity of commonly used ADHD rating scales. Data Selection: Apa PsycInfo, EBSCOHost (Medline), PsycArticle, Sage Journal, and Science Direct databases were searched. Inclusion criteria included peer-reviewed articles, written in English and included (1) participants diagnosed with ADHD, under age 18, and no other neurodevelopmental disorders; (2) blinded classroom observation rating made by trained observers; (3) rating scales completed by teacher and parent; (4) studies included in meta-analysis included Pearson’s r between classroom observations and rating scales. Data Synthesis: Data was selected and extracted by two reviewers, based on PRISMA guidelines (Gates & March, 2016). The combined search yielded 1052 potentially eligible papers. Eleven studies met all criteria for review, and three studies were retained and included in the meta-analysis, comprising ratings for 516 children and adolescents. Meta-analysis used a random-effects model found the combined effect size of all studies of r = 0.2005 (p < 0.001). Additional analysis of heterogeneity found low heterogeneity in the three studies (I2 = 36.27%; Q(df = 15) = 21.5921, p = 0.1190; T2 = 0.0035 (SE = 0.0035)). Conclusions: Our meta-analysis provides evidence in support of the ecological validity of several commonly used behavioral rating scales. More research with multi-informant data, including clinician observations, is needed to further understand the ecological validity of behavioral rating scales when assessing ADHD symptoms.