Occipital Cervical Stabilization Using Occipital Condyles for Cranial Fixation: Technical Case Report

Neurosurgery ◽  
2009 ◽  
Vol 65 (6) ◽  
pp. E1216-E1217 ◽  
Author(s):  
Juan S. Uribe ◽  
Edwin Ramos ◽  
Ali Baaj ◽  
A. Samy Youssef ◽  
Fernando L. Vale

Abstract OBJECTIVE Presentation of a successful case of craniocervical stabilization involving a novel surgical technique using the occipital condyles as the sole cranial fixation points. CLINICAL PRESENTATION A 22-year-old man presented in a delayed fashion with neck pain after a motor vehicle accident. Evaluation revealed a type 2 odontoid fracture with pseudarthrosis and displacement of the dens superiorly and cranial settling of the dens. INTERVENTION The patient underwent posterior occipitocervical fixation with a polyaxial screw rod construct using the occipital condyle, C1 lateral mass, and C2 pars articularis for fixation. The patient had no immediate postoperative deficits. At the time of the 12-month follow-up examination, the patient was neurologically intact with a solid occipitocervical fusion. CONCLUSION Craniocervical stabilization using occipital condyle screws as the sole cephalad fixation points is a feasible option and can be used safely without neurovascular complication in the treatment of craniocervical instability.

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.


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):  
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 ◽  
...  

2002 ◽  
Vol 79 (Supplement) ◽  
pp. 91
Author(s):  
Jeffrey Roth ◽  
Khadija Shahid ◽  
Jerome Sherman ◽  
Jeffrey Cooper

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