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9 results

Surgical Gynecology
  • A Case-Based Approach
  • Edited by Todd R. Jenkins, Lisa Keder, Abimola Famuyide, Kimberly S. Gecsi, David Chelmow
  • Published online:
    19 November 2021
    Print publication:
    02 December 2021
  • Gynecology is principally an operative specialty and the range of gynecologic procedures managed is constantly expanding due to improved instrumentation and minimally invasive techniques. Covering the entire spectrum of gynecologic surgery through a case-based, digestible format, procedures such as vaginal, transvaginal, hysteroscopic, laparoscopic, robotic, and abdominal procedures are included, as well as common surgical challenges and complications. For each case, a clinical vignette outlines the situation and the clinical context of the patient, followed by a comprehensive discussion of the evidence-based management approach and key teaching points. The decision to operate, contraindications, alternatives to surgery and preoperative counseling is discussed in detail along with the level of care needed for each case. Readers will improve their knowledge base of gynecologic surgery and gain multiple tools to overcome common surgical obstacles.

  • Auto-Vetting Transiting Planet Candidates Identified by the Kepler Pipeline

  • Jon M. Jenkins, Sean McCauliff, Christopher Burke, Shawn Seader, Joseph Twicken, Todd Klaus, Dwight Sanderfer, Ashok Srivastava, Michael R. Haas
  • Journal:
    Proceedings of the International Astronomical Union / Volume 8 / Issue S293 / August 2012
    Published online by Cambridge University Press:
    29 April 2014, pp. 94-99
    Print publication:
    August 2012
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  • The Kepler Mission simultaneously measures the brightness of more than 150,000 stars every 29.4 minutes primarily for the purpose of transit photometry. Over the course of its 3.5-year primary mission Kepler has observed over 190,000 distinct stars, announcing 2,321 planet candidates, 2,165 eclipsing binaries, and 105 confirmed planets. As Kepler moves into its 4-year extended mission, the total number of transit-like features identified in the light curves has increased to as many as ~18,000. This number of signals has become intractable for human beings to inspect by eye in a thorough and timely fashion. To mitigate this problem we are developing machine learning approaches to perform the task of reviewing the diagnostics for each transit signal candidate to establish a preliminary list of planetary candidates ranked from most credible to least credible. Our preliminary results indicate that random forests can classify potential transiting planet signatures with an accuracy of more than 98.6% as measured by the area under a receiver-operating curve.