Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
  1. Home
  2. Search

Refine search

Refine search

Access:
Content type:
Publication date:
Apply
Subject: Show more
Journals Show more
Publishers: Show more
Societies Show more
Series: Show more
Collections: Show more

Actions for selected content:

Select all | Deselect all
  • View selected items
  • Export citations
  • Download PDF (zip)
  • Save to Kindle
  • Save to Dropbox
  • Save to Google Drive

Save Search

You can save your searches here and later view and run them again in "My saved searches".

Please provide a title, maximum of 40 characters.
Cancel
×

9 results

Surgical Gynecology
  • 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
  • View description
    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
    • Article
      • You have access
    • PDF
    • Export citation
  • View abstract

    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.