Background: Scholarship remains the principal currency for faculty promotion in academic medicine. Reference points for scholarly growth and productivity at academic medical centers (AMCs) are lacking. Methods: We identified hospital medicine full professors (HMFPs) at AMCs ranked in research by US News & World Report. Scopus was used to identify each HMFP's publications, citations, and Hirsch-index (H-index). Publications; citations; and first, middle, and senior author papers were measured in 3-year intervals postresidency. Scholarly productivity was analyzed by quintile based on publications, AMC research ranking, years postresidency, and grant funding. Results: Data were extracted for 128 HMFPs from 54 AMCs. HMFPs were a mean of 20.5 (SD: 5.4) years postresidency. The median H-index was 7.0 (interquartile range [IQR]: 2.0-16.0); the median number of publications was 15.0 (IQR: 4.0-51.0). Top quintile HMFPs had a median of 175.5 (IQR: 101.5-248.0) publications, whereas fifth quintile HMFPs had a median of 0.0 (IQR: 0.0-1.0) (p < .001). HMFPs on faculty at the top 20 AMCs had a median of 35.5 (IQR: 11.0-108.0) publications, whereas HMFPs in AMCs ranked 81-122 had a median of 3.0 (IQR: 1.0-9.0) (p < .001). Grant-funded HMFPs had a median of 177.0 (IQR: 71.0-278.0) publications, while nongrant-funded HMFPs had a median of 11.0 (IQR: 3.0-25.0) (p < .001). At 3, 6, and 9 years postresidency, HMFPs had a median of 0.0
Effective indexing of multimedia documents requires a multimodal approach in which either the most appropriate modality is selected or different modalities are used in a collaborative fashion. A collaborative pattern is a model of combination between media that defines how and when to combine information coming from different media sources. Fusing information coming from different media seems a natural way to handle multimedia content. We focus on describing fusion strategies where the task is achieved through the use of different modalities. We browse through the literature looking at various state of the art multi-modal fusion techniques varying from naive combination of modalities to more complex methods of machine learning and discuss various issues faced with fusing several modalities having different properties in the context of semantic indexing.
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