Journcal Club Series - Episode 1 - Diagnostic tests (eg, sensitivity and specificity, predictive values, disease prevalence)
Description
Title: Episode 1- Diagnostic tests (eg, sensitivity and specificity, predictive values, disease prevalence)
Target Audience
This activity is directed to physicians who take care of hospitalized children, medical students, nurse practitioners, and physician assistants working in the emergency room, intensive care unit, or hospital wards.
Objectives:
Upon completion of this activity, participants should be able to:
- 1 Review sensitivity and specificity.
- Review predictive values.
- 3 Review disease prevalence vs incidence.
Course Directors:
Tony R. Tarchichi MD — Associate Professor, Department of Pediatrics, Children's Hospital of Pittsburgh of the University of Pittsburgh Medical Center (UPMC.) Paul C. Gaffney Division of Pediatric Hospital Medicine.
No relationships with industry relevant to the content of this educational activity have been disclosed.
Philana Lin M.D., MSc, -- University of Pittsburgh School of Medicine - Associate Professor in the Department of Pediatrics - Pediatric Infectious Disease Division
Dr. Lin receives grant/research support from Pfizer (funds investigator initiated seroprevalance study on invasive pneumococcal infection), and NIH (Investigator initiated research on tuberculosis).
Conflict of Interest Disclosure:
No other planners, members of the planning committee, speakers, presenters, authors, content reviewers and/or anyone else in a position to control the content of this education activity have relevant financial relationships to disclose.
Accreditation Statement:
<img src="data:image/png;base64,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