Retraso diagnóstico en un caso de porfiria aguda intermitente con síntomas psicóticos / Diagnostic delay in a case of acute intermittent porphyria with psychotic symptoms

La porfiria aguda intermitente (PAI) es la forma más frecuente de entre las porfirias agudas. Aunque la presentación típica cursa con ataques de dolor abdominal, en algunos casos aparecen síntomas mentales. Presentamos el caso de una PAI en un paciente varón de 34 años que llevaba 10 años diagnosticado de esquizofrenia paranoide. La atipicidad de los síntomas psicóticos y la concurrencia, durante el último episodio, de clínica neurológica y autonómica, sugirieron el diagnóstico de porfiria. El cuadro se trató exitosamente con la administración de glucosa, lo que permitió la retirada del tratamiento antipsicótico. El diagnóstico se confirmó posteriormente mediante prueba genética. La PAI es una entidad infradiagnosticada por la inespecificidad de su expresión clínica y por las dificultades técnicas en el diagnóstico. La PAI puede ser fácilmente confundida con una enfermedad psiquiátrica, con consecuencias deletéreas. Los profesionales de salud mental deberían conocerla, así como las dificultades que entraña su diagnóstico (AU)

Acute intermittent porphyria (AIP) is the most frequent form of acute porphyria. Although the typical clinical presentation is with abdominal pain, mental symptoms are also present in some cases. The case is presented of a 34-year-old male patient with AIP, who had been diagnosed with paranoid schizophrenia for 10 years. The atypicality of the psychotic symptoms and the concurrence, with neurological and autonomic signs during the last episode, suggested the diagnosis of porphyria. The episode was successfully treated with the administration of glucose, which enabled the antipsychotic treatment to be withdrawn. The diagnosis was later confirmed by genetic testing. AIP is an under-diagnosed condition due to the non-specificity of its clinical expression and the technical difficulties of the diagnosis. AIP can be easily mistaken for a psychiatric illness, with detrimental consequences. Mental health professionals should be aware of this, as well as about the difficulties involved in its diagnosis (AU)

La evaluación del factor de impacto individual de investigadores y centros de investigación utilizando el algoritmo RC / The evaluation of the individual impact factor of researchers and research centers using the RC algorithm

El algoritmo RC evalúa de forma cualitativa el factor de impacto personal de la producción científica de investigadores aislados. Los autores proponen una adaptación de RC para evaluar el factor de impacto personal de centros de investigación, hospitales y otras agrupaciones de investigadores. Así, estas podrían ser clasificadas en función del impacto acreditado de los resultados de su labor científica entre los investigadores de su mismo ámbito científico, algo que podría ser de utilidad para canalizar presupuestos y ayudas a la investigación (AU)

The RC algorithm quantitatively evaluates the personal impact factor of the scientific production of isolated researchers. The authors propose an adaptation of RC to evaluate the personal impact factor of research centers, hospitals and other research groups. Thus, these could be classified according to the accredited impact of the results of their scientific work between researchers of the same scientific area. This could be useful for channelling budgets and grants for research (AU)

The evaluation of the individual impact factor of researchers and research centers using the RC algorithm.

The RC algorithm quantitatively evaluates the personal impact factor of the scientific production of isolated researchers. The authors propose an adaptation of RC to evaluate the personal impact factor of research centers, hospitals and other research groups. Thus, these could be classified according to the accredited impact of the results of their scientific work between researchers of the same scientific area. This could be useful for channelling budgets and grants for research.

Measuring the impact factor of individual researchers in biomedical disciplines.

The authors propose an algorithm for calculating the cumulative personal impact factor of the publications of any researcher whose research activity involves reporting findings in scientific journals or books in the researcher’s field of specialization. This algorithm takes into account the number of times that each published article or book is cited, self-citations, the position of the researcher’s name in the authorship list of each article or book chapter, and the density of this cumulative impact in relation to the researcher’s total production. In addition, it takes into account the type of article or book assessed (review or original research paper), and the length of time since the researcher’s last publication. This algorithm could be useful for the evaluation of the investigational quality of the subjects, in personnel selection processes in which the candidate’s research performance comparisons of the personal scientific influence of various subjects and different research centers.

Evaluación del factor de impacto personal de cada investigador en disciplinas biomédicas / Measuring the impact factor of individual researchers in biomedical disciplines

Los autores proponen un algoritmo para calcular el factor de impacto acumulado personal de las publicaciones de cualquier investigador cuya actividad exija informar de sus hallazgos en revistas científicas y en libros de su especialidad. Dicho algoritmo tiene en cuenta el número de citas que recibe cada artículo y libro publicado, las autocitas, el lugar que ocupa el investigador entre los firmantes de cada artículo o capítulo de libro, así como la densidad de ese impacto en su producción total. Además, tiene en cuenta el tipo de artículo o libro evaluado (revisiones o investigaciones originales), y el tiempo en el que el investigador ha permanecido inactivo. Este algoritmo podría ser útil para la evaluación de la calidad investigadora de los sujetos, en los procesos de selección de personal donde se valore su capacidad investigadora, y permitiría comparar la influencia científica personal entre varios sujetos y la de diferentes centros de investigación (AU)

The authors propose an algorithm for calculating the cumulative personal impact factor of the publications of any researcher whose research activity involves reporting findings in scientific journals or books in the researcher’s field of specialization. This algorithm takes into account the number of times that each published article or book is cited, self-citations, the position of the researcher’s name in the authorship list of each article or book chapter, and the density of this cumulative impact in relation to the researcher’s total production. In addition, it takes into account the type of article or book assessed (review or original research paper), and the length of time since the researcher’s last publication. This algorithm could be useful for the evaluation of the investigational quality of the subjects, in personnel selection processes in which the candidate’s research performance comparisons of the personal scientific influence of various subjects and different research centers (AU)

The Axial Diagnostic Criteria for Depression. Development, construct and predictive validity and reliability.

INTRODUCTION: The authors have developed a new axial diagnostic criterion for depression (ADCD) made up of seven items: mood, motivation/interest, impulse/drive, liking/pleasure, daily job, energy and different quality. They have aimed to examine its predictive validity and reliability, psychometric properties and constructive validity. There are few studies that have examined the psychometric properties of other diagnostic criteria for depression currently in use. MATERIAL AND METHODS: A total of 111 psychiatric outpatients who attended an out-patient clinic consecutively were interviewed. Sixty met the ICD-10 criteria for depressive episode and 51 formed a part of the control group: non-depressed psychiatric outpatients. For the interview, the authors used a brief self-administered questionnaire (IDASD) in which the patients indicated how they felt. Each item had a Visual Analogue Scale so that the subjects could quantify their answers. RESULTS: Four or more items are needed for the ADCD to correctly diagnose depression. At least two of these should belong to a group of three items that were extracted using a discriminant function (mood, energy and different quality). The ADCD constructed in this way has a 0.93 sensitivity and 0.82 specificity, with a kappa reliability of 0.76 and a proportion of total cases correctly classified ranging from 88% to 93%. Specificity reaches up to 0.92 when the control group is formed exclusively by symptom-free psychiatric outpatients. A factor analysis reveals that the ADCD is a one-dimensional model that has good construct validity (0.69). It also has good alpha reliability (alpha = 0.92), elevated consistency of the two halves of the test (R = 0.91) and a high test-retest correlation (r = 0.67). The ADCD diagnostic agreement between two psychiatrists who use the IDASD as a data source is very high (kappa: 1.00). CONCLUSIONS: The ADCD/IDASD system offers a valid and reliable procedure to diagnose depression. It also has an excellent internal architecture, good construct validity and internal consistency. These data are much more than what can be said about other more used diagnostic criteria, which lack this information.