Aggression and disruptive behavior disorders in children and adolescents
Bibliographic record
Abstract
Aggression is a common symptom of many psychiatric disorders including attention deficit hyperactivity disorder, oppositional defiant disorder, conduct disorder, Tourette's disorder, mood disorders (including bipolar disorder), substance-related disorders, alcohol-related disorders, mental retardation, pervasive developmental disorders, intermittent explosive disorder and personality disorders (particularly antisocial personality disorder). Many forms of organic brain disorders may present with aggressive behavior. Aggression is common in some epileptic patients and some endocrinological diseases (e.g., diabetes and hyperthyroidism) may be associated with aggressive behavior. Physicians need to rule out many medical and psychiatric disorders before diagnosing aggressive behavior. A thorough diagnostic work up is the most important step in determining the nature of comorbid disorders associated with the behavioral problem. Structured interviews and rating scales completed by patients, parents, teachers and clinicians may aid the diagnosis and provide quantification for the change process related to treatment. The integration of medication, individual and family counseling, educational and psychosocial interventions including the school and community, may increase the effectiveness of interventions. Due to the common association of aggression and disruptive behaviors with attention deficit hyperactivity disorder, psychostimulants including new generation long-acting medications and other nonstimulant medications are considered the drug of choice for managing aggressive behavior and disruptive behavior disorders. Severe aggressive behavior not responding to these medications may require the single or combined use of mood regulators including lithium and/or antispychotic medications. Drugs such as risperidone (Risperdal, Janssen-Cilag) have documented effectiveness and safety in children and adolescents, and can be used in 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".