The Pharmacological Management of Oppositional Behaviour, Conduct Problems, and Aggression in Children and Adolescents with Attention-Deficit Hyperactivity Disorder, Oppositional Defiant Disorder, and Conduct Disorder: A Systematic Review and Meta-Analysis. Part 2: Antipsychotics and Traditional Mood Stabilizers
Bibliographic record
Abstract
OBJECTIVE: Attention-deficit hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and conduct disorder (CD) are among the most common psychiatric diagnoses in childhood. Aggression and conduct problems are a major source of disability and a risk factor for poor long-term outcomes. METHODS: We performed a systematic review and meta-analysis of randomized controlled trials (RCTs) of antipsychotics, lithium, and anticonvulsants for aggression and conduct problems in youth with ADHD, ODD, and CD. Each medication was given an overall quality of evidence rating based on the Grading of Recommendations Assessment, Development and Evaluation approach. RESULTS: Eleven RCTs of antipsychotics and 7 RCTs of lithium and anticonvulsants were included. There is moderate-quality evidence that risperidone has a moderate-to-large effect on conduct problems and aggression in youth with subaverage IQ and ODD, CD, or disruptive behaviour disorder not otherwise specified, with and without ADHD, and high-quality evidence that risperidone has a moderate effect on disruptive and aggressive behaviour in youth with average IQ and ODD or CD, with and without ADHD. Evidence supporting the use of haloperidol, thioridazine, quetiapine, and lithium in aggressive youth with CD is of low or very-low quality, and evidence supporting the use of divalproex in aggressive youth with ODD or CD is of low quality. There is very-low-quality evidence that carbamazepine is no different from placebo for the management of aggression in youth with CD. CONCLUSION: With the exception of risperidone, the evidence to support the use of antipsychotics and mood stabilizers is of low quality.
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.022 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".