The Psychopharmacology of Aggressive Behavior
Bibliographic record
Abstract
Patients experiencing mental disorders are at an elevated risk for developing aggressive behavior. In the past 10 years, the psychopharmacological treatment of aggression has changed dramatically owing to the introduction of atypical antipsychotics on the market and the increased use of anticonvulsants and lithium in the treatment of aggressive patients.This review (second of 2 parts) uses a translational medicine approach to examine the neurobiology of aggression, discussing the major neurotransmitter systems implicated in its pathogenesis (serotonin, glutamate, norepinephrine, dopamine, and γ-aminobutyric acid) and the neuropharmacological rationale for using atypical antipsychotics, anticonvulsants, and lithium in the therapeutics of aggressive behavior. A critical review of all clinical trials using atypical antipsychotics (aripiprazole, clozapine, loxapine, olanzapine, quetiapine, risperidone, ziprasidone, and amisulpride), anticonvulsants (topiramate, valproate, lamotrigine, and gabapentin), and lithium are presented. Given the complex, multifaceted nature of aggression, a multifunctional combined therapy, targeting different receptors, seems to be the best strategy for treating aggressive behavior. This therapeutic strategy is supported by translational studies and a few human studies, even if additional randomized, double-blind, clinical trials are needed to confirm the clinical efficacy of this framework.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.002 |
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".