Nonepileptic Uses of Antiepileptic Drugs in Children and Adolescents
ABSTRACT Antiepileptic drugs are often prescribed for nonepileptic neurologic and psychiatric conditions. The United States Food and Drug Administration has approved several antiepileptic drugs for the treatment of neuropathic pain, migraine, and mania in adults. For pediatric patients, use of antiepileptic drugs for non-seizure-related purposes is supported mainly by adult studies, open-label trials, and case reports. Summarized here is the published literature for or against the use of antiepileptic drugs for neuropathic pain, migraine, movement disorders, bipolar disorder, aggressive behavior, and pervasive developmental disorders in children and adolescents. Using the American Academy of Neurology's four-tiered classification scheme for a therapeutic article and translation to a recommendation rating, there are no nonepileptic disorders for which antiepileptic drugs have been established as effective for pediatric patients. Valproate and carbamazepine are "possibly effective" in the treatment of Sydenham chorea, and valproate is "probably effective" in decreasing aggressive behavior. Carbamazepine is "probably ineffective" in the treatment of aggression, and lamotrigine is "possibly ineffective" in improving the core symptom of pervasive developmental disorders. Despite the frequent use of antiepileptic drugs in the treatment of juvenile bipolar disorder, migraine, and neuropathic pain, the data are insufficient to make recommendations regarding the efficacy of antiepileptics in these conditions in children and adolescents.
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ABSTRACT: Pediatric chronic pain is widespread, under-recognized and undertreated. Best management usually involves a multimodal approach coordinated by a multidisciplinary team. The present commentary specifically discusses common pharmacological approaches to chronic pain in children, identifies gaps in knowledge and suggests several research directions that would benefit future clinical care.Pain research & management: the journal of the Canadian Pain Society = journal de la societe canadienne pour le traitement de la douleur 01/2013; 18(1):47-50. · 1.39 Impact Factor
Conference Paper: Fuzzy clustering using extended MFA for continuous-valued state space[Show abstract] [Hide abstract]
ABSTRACT: In classical clustering, an item must belong to any one cluster, whereas fuzzy clustering describes more accurately the ambiguous type of structure in data. MFA (mean field annealing) combines characteristics of simulated annealing and a neural network, and exhibits the rapid convergence of the neural network, while preserving the solution quality afforded by SSA (stochastic simulated annealing). An extended MFA algorithm to solve the fuzzy clustering problem is proposed. It has continuous-value state space. The results of the experiment are given and compared with those of the fuzzy ISODATA algorithm. Fuzzy clustering using the MFA algorithm shows a lower energy state than that of the fuzzy ISODATA algorithm. The perturbing of only one variable is simpler and faster than traditional SSA method to perturb all the variables together, and ultimately enables true parallelismNeural Networks, 1992. IJCNN., International Joint Conference on; 07/1992
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