RESUMEN
Congenital infantile fibrosarcoma is a rare soft tissue neoplasm in the infant of which only a few cases are reported as congenital. This tumor has a rapid growth and extensive local invasion, but metastasis rarely occurs. Distal extremities involvement is more common and metastasis are rare. We report a case of a congenital infantile fibrosarcoma of the forearm, which was initially confused with a hemangioma, treated successfully by surgical resection.
Asunto(s)
Fibrosarcoma/congénito , Fibrosarcoma/diagnóstico , Antebrazo/patología , Neoplasias de los Tejidos Blandos/congénito , Neoplasias de los Tejidos Blandos/diagnóstico , Diagnóstico Diferencial , Fibrosarcoma/patología , Fibrosarcoma/cirugía , Antebrazo/cirugía , Hemangioma/diagnóstico , Humanos , Recién Nacido , Neoplasias de los Tejidos Blandos/patología , Neoplasias de los Tejidos Blandos/cirugía , Resultado del TratamientoRESUMEN
INTRODUCTION: The Brief Psychiatric Rating Scale was initially developed as a rapid method to assess symptom change in psychiatric inpatients of various diagnoses. The original version was expanded to an 18-item version and thereafter to a 24-item version to increase sensitivity to a broader range of psychotic and affective symptoms. The latest version of the expanded 24- item BPRS provides probe questions and detailed anchor points for the ratings for each item. LITERATURE FINDINGS: Studies have shown the expanded and anchored 24-item BPRS to be a sensitive and effective measure of psychiatric symptoms with good interrater reliability that can be maintained over time. To our knowledge, there are eight published papers including factor analyses of the BPRS-E(A). While many similarities are evident between these studies, inconsistencies are apparent that may have been due to sample size, characteristics and / or methodological differences in the factor analysis computation. Among these studies, six provided a four-factor solution. There was no French version of this scale available. METHODS: After its translation into French and back translation, we investigated the validity of the French BPRS-E(A) version. We carried out a component analysis on the data of 111 participants of various diagnoses, mostly hospitalised for a first psychotic episode, yielding to a three-factor solution (positive symptoms--disorganisation; depression-anxiety and negative symptoms). RESULTS: A good internal consistency and interrater reliability were found. These results confirm the psychometric value of the BPRS-E(A) in its French version. We compared those findings to earlier reports; similarities and differences are discussed.
Asunto(s)
Escalas de Valoración Psiquiátrica Breve/estadística & datos numéricos , Comparación Transcultural , Trastornos Psicóticos/diagnóstico , Adulto , Síntomas Afectivos/diagnóstico , Síntomas Afectivos/psicología , Síntomas Afectivos/terapia , Trastornos de Ansiedad/diagnóstico , Trastornos de Ansiedad/psicología , Trastornos de Ansiedad/terapia , Trastorno Bipolar/diagnóstico , Trastorno Bipolar/psicología , Trastorno Bipolar/terapia , Trastorno Depresivo/diagnóstico , Trastorno Depresivo/psicología , Trastorno Depresivo/terapia , Femenino , Francia , Hospitalización , Humanos , Estudios Longitudinales , Masculino , Persona de Mediana Edad , Psicometría/estadística & datos numéricos , Trastornos Psicóticos/psicología , Trastornos Psicóticos/terapia , Reproducibilidad de los Resultados , Esquizofrenia/diagnóstico , Esquizofrenia/terapia , Psicología del Esquizofrénico , Ajuste Social , Traducción , Resultado del Tratamiento , Estados Unidos , Adulto JovenRESUMEN
A new neural network-based approach is introduced for recursive computation of the principal components of a stationary vector stochastic process. The neurons of a single-layer network are sequentially trained using a recursive least squares squares (RLS) type algorithm to extract the principal components of the input process. The optimality criterion is based on retaining the maximum information contained in the input sequence so as to be able to reconstruct the network inputs from the corresponding outputs with minimum mean squared error. The proof of the convergence of the weight vectors to the principal eigenvectors is also established. A simulation example is given to show the accuracy and speed advantages of this algorithm in comparison with the existing methods. Finally, the application of this learning algorithm to image data reduction and filtering of images degraded by additive and/or multiplicative noise is considered.