Aspirated teeth - rigid or flexible bronchoscopy

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.

Author(s):  
Jack Porrino ◽  
Alvin R. Wyatt

Chapter 29 discusses foreign bodies and trauma. An object that originates from outside the body is by definition considered a foreign body. The retained foreign body can occur in a variety of clinical settings, such as motor vehicle accident, explosion, or gunshot injury and is a common presenting complaint in the acute care setting. Although radiography is often obtained as the first line of imaging in the diagnostic workup of soft tissue foreign bodies, some object compositions, such as wood and plastic, are radiolucent. In this scenario, US is an excellent imaging modality in identifying a retained soft tissue foreign body and can also assist in its removal. Management of the superficial foreign body is typically uneventful, however, the deeply penetrating foreign body may require a more intricate surgical procedure because of proximity of adjacent vital structures.


2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock

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.


Author(s):  
Tal Margaliot Kalifa ◽  
Misgav Rottenstreich ◽  
Eyal Mazaki ◽  
Hen Y. Sela ◽  
Schwartz Alon ◽  
...  

2021 ◽  
Author(s):  
Gaia S. Pocobelli ◽  
Mary A. Akosile ◽  
Ryan N. Hansen ◽  
Joanna Eavey ◽  
Robert D. Wellman ◽  
...  

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