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A machine learning analysis of patient concerns regarding mastopexy

  • Christopher James Didzbalis
    Affiliations
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Rutgers-New Jersey Medical School, 140 Bergen Street, Suite E1620, Newark, NJ 07103, United States
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  • Christopher C. Tseng
    Affiliations
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Rutgers-New Jersey Medical School, 140 Bergen Street, Suite E1620, Newark, NJ 07103, United States
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  • Joseph Weisberger
    Affiliations
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Rutgers-New Jersey Medical School, 140 Bergen Street, Suite E1620, Newark, NJ 07103, United States
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  • Amon-Ra Gama
    Affiliations
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Rutgers-New Jersey Medical School, 140 Bergen Street, Suite E1620, Newark, NJ 07103, United States
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  • Edward S. Lee
    Correspondence
    Corresponding author at: Chief, Division of Plastic and Reconstructive Surgery, Department of Surgery, Rutgers-New Jersey Medical School, Doctor's Office Center (DOC), 90 Bergen Street Room 7400, Newark, NJ 07101, United States.
    Affiliations
    Division of Plastic and Reconstructive Surgery, Department of Surgery, Rutgers-New Jersey Medical School, 140 Bergen Street, Suite E1620, Newark, NJ 07103, United States
    Search for articles by this author
Published:October 10, 2022DOI:https://doi.org/10.1016/j.bjps.2022.10.007

      Summary

      Background

      Social media plays an important role in connecting patients and plastic surgeons. We utilized patient inquiries regarding mastopexy from an online social media site to determine the most prevalent patient concerns, while employing a machine-learning algorithm to generate the questions representative of the dataset.

      Objective

      This data allow plastic surgeons to better tailor their preoperative consultations to address common concerns, set realistic expectations, and improve overall satisfaction.

      Methods

      A total of 2,011 inquiries from the mastopexy section of Realself.com were obtained using an open-source web crawler. Each inquiry was manually categorized as preoperative or postoperative and classified into subcategories based upon the free text entry. Lastly, questions were analyzed using machine learning to determine ten questions most representative of the inquiry pool.

      Results

      Of the 2,011 inquiries analyzed, 52.91% were preoperative and 47.09% were postoperative. Most preoperative questions asked about procedure eligibility (309, 29.04%), surgical techniques and logistics (260, 24.44%), and the best type of breast lift for the user (259, 24.34%). Among postoperative questions, questions regarding appearance were the most common (491, 51.85%), followed by symptoms after surgery (197, 19.75%) and behavior allowed/disallowed (145, 15.31%). Appearance was further subcategorized with the most common categories being appearance of the nipple (98, 19.86%), skin discoloration (88, 17.92%), and scarring (74, 15.07%).

      Conclusion

      By utilizing the data that social media websites, like Realself.com, provide, plastic surgeons can better understand common patient concerns. This data aid in optimizing the preoperative consultation process to address the common concerns, recalibrate unrealistic expectations, and improve overall satisfaction.

      Keywords

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