Depression in children and adolescents with ADHD : a systematic literature review
Bibliographic record
Abstract
Introduction: Poor mental health in children and adolescents is a growing public health concern. Attention Deficit and Hyperactivity Disorder (ADHD) is a prevalent disorder in youth, impacting their mental well-being. Understanding the link between ADHD and comorbid disorders such as depression is crucial for the treatment of children and adolescents with ADHD. Aim: The aim was to perform a systematic literature review to explore the type of available studies and if there is an increased risk of depression among children and adolescents with ADHD compared to controls. Materials and methods: PubMed and PsycInfo identified 18 relevant articles (January 2014 to October 2023). Inclusion criteria required a diagnosis of ADHD and depression or relevant symptoms. The Newcastle Ottawa Scale evaluated risk of bias and data were extracted for analysis. Results: Out of 1,136,320 participants in the 18 studies, 83% reported a statistically significant higher rate of depression or depressive symptoms in individuals with ADHD. The risk varied, with hazard ratios in two larger studies ranging from 7.16 to 8.64. Risk of bias also varied, emphasizing the need for cautious interpretation. Discussion: The findings highlight the need for thorough assessment of depression symptoms in children and adolescents with ADHD. The study emphasizes the importance of updating guidelines to include specific screening for depression in this population and the potential benefits of early intervention to prevent or alleviate comorbid depression. Conclusion: This study reinforces the significant ADHD-depression association in children and adolescents, urging increased attention and intervention strategies for improved care and treatment.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".