Artículos de revistas
Shared Decision Making in Pediatrics: A Systematic Review and Meta-analysis
BACKGROUND: Little is known about the impact of interventions to support shared decision making (SDM) with pediatric patients. OBJECTIVES: To summarize the efficacy of SDM interventions in pediatrics on patient-centered outcomes. DATA SOURCES: We searched Ovid Medline, Ovid Embase, Ovid Cochrane Library, Web of Science, Scopus, and Ovid PsycInfo from database inception to December 30, 2013, and performed an environmental scan. STUDY ELIGIBILITY CRITERIA: We included interventions designed to engage pediatric patients, parents, or both in a medical decision, regardless of study design or reported outcomes. STUDY APPRAISAL AND SYNTHESIS METHODS: We reviewed all studies in duplicate for inclusion, data extraction, and risk of bias assessment. Meta-analysis was performed on 3 outcomes: knowledge, decisional conflict, and satisfaction. RESULTS: Sixty-one citations describing 54 interventions met eligibility criteria. Fifteen studies reported outcomes such that they were eligible for inclusion in meta-analysis. Heterogeneity across studies was high. Meta-analysis revealed SDM interventions significantly improved knowledge (standardized mean difference [SMD] 1.21, 95% confidence interval [CI] 0.26 to 2.17, P = .01) and reduced decisional conflict (SMD -1.20, 95% CI -2.01 to -0.40, P = .003). Interventions showed a nonsignificant trend toward increased satisfaction (SMD 0.37, 95% CI -0.04 to 0.78, P = .08). LIMITATIONS: Included studies were heterogeneous in nature, including their conceptions of SDM. CONCLUSIONS AND IMPLICATIONS OF KEY FINDINGS: A limited evidence base suggests that pediatric SDM interventions improve knowledge and decisional conflict, but their impact on other outcomes is unclear.
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Kawaoku, F. M.; Araujo, J. E.; Kienitz, K. H.; Mesquita, L.
Instituto Tecnológico de Aeronáutica (ITA); Instituto Nacional de Pesquisas Espaciais (INPE); Universidade Estadual Paulista (UNESP) (2005-12-01)An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose ...
Fuentes P., Miguel A.