Special Topics in Adolescents

2014 ◽  
pp. 125-126
Author(s):  
David L Brody
Keyword(s):  
Decision Making ◽  
Learning Disabilities ◽  
Attention Deficit ◽  
Peer Influences ◽  
Mood Instability

Adolescents may be less able to make good judgments about their own abilities than adults. The collateral source becomes even more important than usual. Address questions about drug and alchohol use privately and give advice without the parents present. Peer influences may have a big impact on decision-making. Obtain collateral history from peers and educate peers as well as parents. Preexisting attention deficit, learning disabilities, and mood instability can get substantially worse after concussion and may require intensified treatment. For patients at the cusp of starting to drive, consider advising extra caution: go back to the beginning of driver’s education and get a professional driving evaluation. Consider preemptively addressing questions that the adolescent may be afraid to ask or cannot formulate accurately.

Concussion in Adolescents

2019 ◽  
pp. 180-183
Author(s):  
David L. Brody
Keyword(s):  
Decision Making ◽  
Learning Disabilities ◽  
Attention Deficit ◽  
Return To Play ◽  
Peer Influences ◽  
Mood Instability ◽  
Reduce Risk ◽  
Drug And Alcohol Use ◽  
Drug And Alcohol ◽  
Impact Syndrome

Adolescents should follow the no return to play for 24 hours rule strictly to improve outcomes and reduce risk of second impact syndrome. Because adolescents may be less able to make good judgments about their own abilities than adults, the collateral source becomes even more important than usual. Address questions about drug and alcohol use privately and give advice without the parents present. Peer influences may have a big impact on decision-making. Obtain collateral history from peers and educate peers as well as parents. Preexisting attention deficit, learning disabilities, and mood instability can get substantially worse after concussion and may require intensified treatment. For patients on the cusp of starting to drive, consider advising extra caution: go back to the beginning of driver’s education and get a professional driving evaluation. Consider preemptively addressing questions that the adolescent may be afraid to ask or cannot formulate accurately.

scholarly journals Shifting uncertainty intolerance: methylphenidate and attention-deficit hyperactivity disorder

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alekhya Mandali ◽  
Arjun Sethi ◽  
Mara Cercignani ◽  
Neil A. Harrison ◽  
Valerie Voon
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Attention Deficit ◽  
Computational Models ◽  
Risky Choice ◽  
Risk Tolerance ◽  
Healthy Population ◽  
Hyperactivity Disorder ◽  
Risky Choices ◽  
Cardinal Symptom

AbstractRisk evaluation is a critical component of decision making. Risk tolerance is relevant in both daily decisions and pathological disorders such as attention-deficit hyperactivity disorder (ADHD), where impulsivity is a cardinal symptom. Methylphenidate, a commonly prescribed drug in ADHD, improves attention but has mixed reports on risk-based decision making. Using a double-blinded placebo protocol, we studied the risk attitudes of ADHD patients and age-matched healthy volunteers while performing the 2-step sequential learning task and examined the effect of methylphenidate on their choices. We then applied a novel computational analysis using the hierarchical drift–diffusion model to extract parameters such as threshold (‘a’—amount of evidence accumulated before making a decision), drift rate (‘v’—information processing speed) and response bias (‘z’ apriori bias towards a specific choice) focusing specifically on risky choice preference. Critically, we show that ADHD patients on placebo have an apriori bias towards risky choices compared to controls. Furthermore, methylphenidate enhanced preference towards risky choices (higher apriori bias) in both groups but had a significantly greater effect in the patient population independent of clinical scores. Thus, methylphenidate appears to shift tolerance towards risky uncertain choices possibly mediated by prefrontal dopaminergic and noradrenergic modulation. We emphasise the utility of computational models in detecting underlying processes. Our findings have implications for subtle yet differential effects of methylphenidate on ADHD compared to healthy population.

scholarly journals Disentangling ADHD's Presentation-Related Decision-Making—A Meta-Analytic Approach on Predominant Presentations

2021 ◽  
Vol 12 ◽  
Author(s):  
Marcel Schulze ◽  
David Coghill ◽  
Silke Lux ◽  
Alexandra Philipsen
Keyword(s):  
Decision Making ◽  
Attention Deficit Hyperactivity Disorder ◽  
Reinforcement Learning ◽  
Risk Taking ◽  
Attention Deficit ◽  
Meta Analysis ◽  
Analytic Approach ◽  
Effect Sizes ◽  
Hyperactivity Disorder ◽  
The Relationship

Background: Deficient decision-making (DM) in attention deficit/hyperactivity disorder (ADHD) is marked by altered reward sensitivity, higher risk taking, and aberrant reinforcement learning. Previous meta-analysis aggregate findings for the ADHD combined presentation (ADHD-C) mostly, while the ADHD predominantly inattentive presentation (ADHD-I) and the predominantly hyperactive/impulsive presentation (ADHD-H) were not disentangled. The objectives of the current meta-analysis were to aggregate findings from DM for each presentation separately.Methods: A comprehensive literature search of the PubMed (Medline) and Web of Science Database took place using the keywords “ADHD,” “attention-deficit/hyperactivity disorder,” “decision-making,” “risk-taking,” “reinforcement learning,” and “risky.” Random-effects models based on correlational effect-sizes were conducted. Heterogeneity analysis and sensitivity/outlier analysis were performed, and publication biases were assessed with funnel-plots and the egger intercept.Results: Of 1,240 candidate articles, seven fulfilled criteria for analysis of ADHD-C (N = 193), seven for ADHD-I (N = 256), and eight for ADHD-H (N = 231). Moderate effect-size were found for ADHD-C (r = 0.34; p = 0.0001; 95% CI = [0.19, 0.49]). Small effect-sizes were found for ADHD-I (r = 0.09; p = 0.0001; 95% CI = [0.008, 0.25]) and for ADHD-H (r = 0.1; p = 0.0001; 95% CI = [−0.012, 0.32]). Heterogeneity was moderate for ADHD-H. Sensitivity analyses show robustness of the analysis, and no outliers were detected. No publication bias was evident.Conclusion: This is the first study that uses a meta-analytic approach to investigate the relationship between the different presentations of ADHD separately. These findings provide first evidence of lesser pronounced impairment in DM for ADHD-I and ADHD-I compared to ADHD-C. While the exact factors remain elusive, the current study can be considered as a starting point to reveal the relationship of ADHD presentations and DM more detailed.

scholarly journals Intelligent Predicting Learning Disabilities in School Going Children using Fuzzy Logic K Mean Clustering in Machine Learning.

2019 ◽  
Vol 8 (4) ◽  
pp. 1694-1698
Keyword(s):  
Social Sciences ◽  
Machine Learning ◽  
Decision Making ◽  
Learning Disabilities ◽  
Fuzzy Logic ◽  
Side Effects ◽  
Early Analysis ◽  
Comprehensive Investigation ◽  
Engineering Statistics ◽  
Decision Making System

Learning disabilities (LD) is turning into a major issue in various nations around the globe which can even contrarily influence human common advancement. The undertaking of this work is to help the specialized programme network in their task to be with the standard. The underlying section of the paper gives a comprehensive investigation of the distinctive components of diagnosing learning disabilities. Despite the fact that LD can be analysed early - before 5 years of age, most youngsters were not determined to have LD until the age of nine on account of its unpredictable side effects and unclear indication in children disorder issue. Fuzzy logic K-means clustering has inspired a tremendous transformation in Machine learning and can take and able to resolve a variation of problems. This paper is the elaboration on the strategy for utilizing this mix to encourage the early analysis of LD. Since Fuzzy Logic clustering in Machine Learning is generally considered and connected in different areas of science, we invite all the related analysts from the fields of computer science, engineering, statistics, social sciences, healthcare, and so on, etc. The result of the paper demonstrates that the previously mentioned methodology can possibly be the potential of the supporting decision-making system in LD investigating and diagnosing.

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