Management of Adolescents and Young Adults With Attention-Deficit/Hyperactivity Disorder
Bibliographic record
Abstract
. Adolescence and young adulthood can be a period of immense change. Navigating this critical period of development can be particularly challenging, even for healthy individuals, given the immense cognitive, emotional, neurobiological, physical, and social changes taking place during this time. The additional burden of the core symptoms of attention-deficit/ hyperactivity disorder (ADHD) and associated functional impairments can further complicate this transitional period through to full adulthood. In this review, we focus on the distinctive behavioral and neurobiological characteristics of adolescents and young adults (AYAs) with ADHD. We discuss the variety of contemporary challenges faced by these patients as they transition to full adult maturity. A comprehensive literature search of PubMed was performed on February 23, 2022. We searched for English language peer-reviewed articles published in the previous 10 years using primary search terms including "attention deficit hyperactivity disorder," "adolescent," and "young adult." Importantly, we provide various innovative and practical strategies and solutions to overcome the challenges faced by AYAs with ADHD, with the aim of improving the management of this unique patient population. This review is intended to support physicians less familiar with the management and treatment of AYAs with ADHD. .
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".