Efficacy of Topiramate, Valproate, and Their Combination on Aggression/Agitation Behavior in Patients With Psychosis
Bibliographic record
Abstract
Topiramate is an antiepileptic drug, recently also used in the treatment of psychiatric diseases. Inasmuch as topiramate and valproate, which are currently used for aggressive behavior, share several pharmacological mechanisms (positive modulatory effect on the GABA activity and negative modulatory effect on glutamatergic neurotransmission), the objective of the present study was to compare the pharmacological effects of topiramate with those of valproate and their combination in patients with psychiatric disorders showing marked aggression and agitation. A retrospective, case-controlled, mirror-image study was carried out in a sample of 45 inpatients affected by schizophrenia, schizoaffective and bipolar disorder, and hospitalized in a maximum-security Canadian psychiatry hospital. Overt Aggression Scale, Agitation-Calmness Evaluation Scale, number and intensity of psychotic episodes, number of episodes of withdrawal from group activities per week, and number of therapeutic isolation per week and of strict surveillance intervention per week were evaluated before and after the treatments. Results indicate that patients treated with topiramate show a decrease in the average score of the Overt Aggression Scale, a decrease of episodes of agitation and of strict surveillance interventions. This effect was similar to the group treated with valproate or with the combination of valproate-topiramate. However, valproate therapy, but not topiramate therapy, decreased the intensity of agitation episodes measured by the Agitation-Calmness Evaluation Scale; valproate and the combination topiramate-valproate decreased the number of psychotic disorganization episodes as well. These results suggest that topiramate could be a valid medicine in the control of aggression in psychosis. Double-blind, randomized, placebo-controlled studies need to further assess this pharmacological indication.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".