Recursive subsetting to identify patients in the STAR*D / Anthony Y. C. Kuk en The Journal of Clinical Psychiatry, Año 2010 - Vol. 71 - No. 11 (Noviembre)

Público ISBD [artículo]  inThe Journal of Clinical Psychiatry > Año 2010 - Vol. 71 - No. 11 (Noviembre) . - pp. 1502-1508 Título : Recursive subsetting to identify patients in the STAR*D : a method to enhance the accuracy of early prediction of treatment outcome and to inform personalized care Tipo de documento: texto impreso Autores: Anthony Y. C. Kuk, Autor ; Jialiang Li, Autor ; Augustus John Rush, Autor Fecha de publicación: 2022 Artículo en la página: pp. 1502-1508 Idioma : Inglés (eng) Idioma original : Inglés (eng) Palabras clave: Citalopram, Trastorno depresivo - terapia farmacológica, Trastorno depresivo mayor, Medicina de Precisión, Inhibidores de la captación de serotonina. Resumen: There are currently no clinically useful assessments that can reliably predict--early in treatment--whether a particular depressed patient will respond to a particular antidepressant. We explored the possibility of using baseline features and early symptom change to predict which patients will and which patients will not respond to treatment. Link: ./index.php?lvl=notice_display&id=28259 [artículo] Recursive subsetting to identify patients in the STAR*D : a method to enhance the accuracy of early prediction of treatment outcome and to inform personalized care [texto impreso] / Anthony Y. C. Kuk, Autor ; Jialiang Li, Autor ; Augustus John Rush, Autor . - 2022 . - pp. 1502-1508. Idioma : Inglés (eng) Idioma original : Inglés (eng) in The Journal of Clinical Psychiatry > Año 2010 - Vol. 71 - No. 11 (Noviembre) . - pp. 1502-1508 Palabras clave: Citalopram, Trastorno depresivo - terapia farmacológica, Trastorno depresivo mayor, Medicina de Precisión, Inhibidores de la captación de serotonina. Resumen: There are currently no clinically useful assessments that can reliably predict--early in treatment--whether a particular depressed patient will respond to a particular antidepressant. We explored the possibility of using baseline features and early symptom change to predict which patients will and which patients will not respond to treatment. Link: ./index.php?lvl=notice_display&id=28259

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