Dollars for Profs
Dig Into University Researchers' Outside Income and Conflicts of Interest
Published Dec. 6, 2019
This database was last updated in December 2019 and should only be used as a historical snapshot. There may be new or amended records not reflected here.
Conflict of Interest
Institutions must file significant disclosures to the National Institutes of Health if they determine financial relationships could affect the design, conduct or reporting of the NIH-funded research. The NIH provided us with their entire financial conflict of interest database, with filings from 2012 through 2019.
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Marian Bartlett
University of California, San Diego, Department: Biostatistics & Other Math Sci
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Emotient
Equity Interest - Non-publicly traded entity ( e.g., stock, stock option, or other ownership interest)
Dr. Bartlett is a company founder, holds a (b)(4) equity interest in the company and is lead scientist for the company while she is on administrative leave from the University. In addition, the company has licensed technology from the University and Dr. Bartlett is the inventor. The goal of the research project is to develop and evaluate an automated Novel Tool for the Assessment of Pediatric Pain (NTAP) that utilizes novel computer vision and wearable physiology sensor technologies to estimate pain severity in children. Dr. Bartlett will lead the project and coordinate the efforts of all research.
Development of a new technology for assessing pediatric pain (NTAP)
Suboptimal pain assessment in children is unfortunately common and results in unrelieved pain, and untreated pain contributes to significant morbidity and mortality in children. The World Health Organization and other health organizations have mandated that health entities recognize the rights of children to have their pain alleviated; in order to accomplish this goal, a more reliable and accurate method for pain assessment in this at-risk population is needed. Emerging technologies with their high potential for standardization and reproducibility, as well as grounding in empirical data through machine learning, are uniquely qualified for clinical pain assessment. We will develop and test an automated tool that utilizes novel computer vision and wearable physiology sensor technologies to estimate pain severity in children.
Filed on December 17, 2013.
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Notes: When a more specific filing date is not available for an individual financial disclosure or conflict of interest form, we use the year the form was filed. If the year was not disclosed, we report the range of years covered by our public records requests. In a few cases, a start date was provided instead of a filing date. In those cases, we use the start date instead.
Fewer than 10% of records from the University of Florida and fewer than 1% of records from the University of Texas system were removed because they did not contain enough information.
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