scholarly journals Hemoperitoneum following a Motor Vehicle Accident in a Patient with 20 Uterine Leiomyomas

2020 ◽  
Vol 3 (2) ◽  
pp. 29-34
Author(s):  
Gregory Wu ◽  
Olivier Urayeneza ◽  
Gudata Hinika
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Rare Complication ◽  
Uterine Leiomyomas ◽  
Reproductive Age ◽  
Severe Bleeding ◽  
Emergency Setting ◽  
Vehicle Accident ◽  
Emergency Hysterectomy ◽  
Pelvic Pressure

Uterine leiomyomas are neoplasms of the smooth muscle that can cause complications such as severe bleeding and infertility in women of reproductive age. While many individuals may be asymptomatic, others may present with anemia secondary to heavy bleeding, cyclical abdominal pain, pelvic pressure, and urinary or bowel symptoms. A rare complication of uterine leiomyomas is avulsion due to blunt abdominal trauma resulting in hemoperitoneum. We present a 49-year-old female with no pertinent medical history who presented to the emergency room following a motor vehicle accident. Computed tomography scan revealed extensive hemoperitoneum and the patient was taken to the operating room where the source of bleeding was identified as multiple avulsed leiomyomas. The patient underwent an emergency hysterectomy and bilateral salpingectomy. Pathology reported a uterus weighing 6,000 g and the presence of 20 leiomyomas, with the largest measuring 29 cm. Knowledge of leiomyoma symptoms, presentation, and complications by both the patient and clinicians may help identify diagnoses and expedite intervention in the emergency setting.

scholarly journals Positive Seatbelt Sign with Avulsed Leiomyoma following Motor Vehicle Accident Leading to Hemoperitoneum

2018 ◽  
Vol 2018 ◽  
pp. 1-4 ◽  
Author(s):  
Martin A. C. Manoukian ◽  
Amode R. Tembhekar ◽  
Sarah E. Medeiros
Keyword(s):  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Incidental Finding ◽  
Major Complication ◽  
Exploratory Laparotomy ◽  
Fresh Frozen Plasma ◽  
Reproductive Age ◽  
Packed Red Blood Cells ◽  
Vehicle Accident ◽  
Increased Risk

A positive seatbelt sign following a motor vehicle accident is associated with an increased risk of intra-abdominal injury and hemoperitoneum. Injury to the uterus in reproductive-age women can also occur. In this report, we describe a 29-year-old nulligravida female who presented to the emergency room following a motor vehicle accident at freeway speeds. A positive seatbelt sign was noted, and a focused assessment with sonography for trauma revealed hemoperitoneum with an incidental finding of uterine leiomyomata. Upon exploratory laparotomy, a free-floating intraperitoneal mass was identified as an avulsed uterine leiomyoma. A uterine laceration containing a subserosal leiomyoma was also identified. The gynecological team was consulted, and a myomectomy of the subserosal leiomyoma followed by a closure of the uterine laceration was performed. The patient was transfused with a total of three units of packed red blood cells and two units of fresh frozen plasma. The postoperative course was without major complication. A positive seatbelt sign and hemoperitoneum in a reproductive-age woman with leiomyomata should increase the clinical suspicion for uterine injury and decrease the threshold for obtaining a gynecological consultation.

Virtual reality and computer games in the treatment of driving phobia induced by a motor vehicle accident

2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim
Keyword(s):  
Virtual Reality ◽  
Computer Games ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

Aspirated teeth - rigid or flexible bronchoscopy

2019 ◽  
pp. 1-2
Keyword(s):  
Clinical Practice ◽  
Foreign Bodies ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Flexible Bronchoscopy ◽  
Vehicle Accident

Tracheobronchial foreign bodies are a common problem in clinical practice. We present the case of a patient with three aspirated teeth following a motor vehicle accident.

Isolated second trimester uterine rupture following a motor vehicle accident

2021 ◽  
Vol 258 ◽  
pp. 461-463
Author(s):  
Tal Margaliot Kalifa ◽  
Misgav Rottenstreich ◽  
Eyal Mazaki ◽  
Hen Y. Sela ◽  
Schwartz Alon ◽  
...  
Keyword(s):  
Uterine Rupture ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Second Trimester ◽  
Vehicle Accident

scholarly journals Obstructive Sleep Apnea and Risk of Motor Vehicle Accident

2021 ◽  
Author(s):  
Gaia S. Pocobelli ◽  
Mary A. Akosile ◽  
Ryan N. Hansen ◽  
Joanna Eavey ◽  
Robert D. Wellman ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident ◽  
Obstructive Sleep
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