Second-Generation Antipsychotics for the Treatment of Disruptive Behaviour Disorders in Children: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: The use of second-generation antipsychotics (SGAs) in youth has increased considerably. Increases are mainly attributable to treatment of disruptive behaviour disorders (DBDs). Our objective was to review the evidence regarding the efficacy of SGAs for DBDs in youth. METHOD: We performed a systematic review of all randomized controlled trials (RCTs) of SGAs and placebo for the treatment of DBDs in youth, focusing on efficacy data. RESULTS: Eight RCTs in youth with DBDs were included. Five RCTs evaluated the use of risperidone in youth with the combination of subaverage-borderline IQ and disruptive behaviour-aggression. Single RCTs evaluated the use of risperidone for treatment-resistant aggression in attention-deficit hyperactivity disorder and for the treatment of conduct disorder (CD), and a single RCT evaluated the use of quetiapine for adolescent CD. The efficacy results of each of these studies are described. CONCLUSION: Four placebo-controlled studies support the short-term efficacy of low-dose risperidone in youth with a subaverage IQ. Placebo-controlled evidence is weak or nonexistent for SGAs other than risperidone, and is weak in youth with an average IQ. Multiple factors likely account for the disconnect between this limited evidence base and the frequent use of SGAs for DBDs in clinical practice. These include extrapolation from studies in youth with autism or a subaverage IQ to normally developing youth; ease of SGA titration and the mistaken perception that little monitoring is required; unavailability of psychosocial treatments; limited familiarity with other pharmacological options; clinical and cultural norms; and the influence of the pharmaceutical industry.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".