Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Background/aim: Impact to the orofacial region, in particular teeth, is a frequent incident leading to injury in many sports and can result in health and economic costs for the injured individual. The majority of previous work has applied synthetic models such as plaster or stone, to form analogs of relevant structures to study the potential for impact-induced injury. Biomechanical studies that have applied tissue models (animal or human) for the purpose of determining the biomechanical measures associated with dental injury are rare. The aim of this study was to apply a simple ex vivo model based on swine dentition to ascertain which of a select list of measurable quantities associated with impact mechanics could predict luxation and fracture of teeth due to impact.
Methods: Mandibular central incisors of ex vivo swine dentitions were impacted using a linear drop tower with heights ranging from 1.20 m to 2.42 m. Seven mechanical predictors were assessed at impact and were then subjected to binary logistic regression techniques to determine which was the best predictor of luxations or fractures of the teeth.
Results: Of the seven mechanical predictors, (1) the velocity of the impacting body (R = 0.477), (2) a proxy measure for the change in kinetic energy of the impacting body (R = 0.586), and (3) the approximate energy absorbed by the tissue (R = 0.722) were found to be statistically significantly different (p < .05), offering the greatest specificity as indicated by receiver operator characteristics. Other measures that are frequently used in impact mechanics, including peak linear acceleration and velocity change, were not statistically significant predictors of tooth injury.
Conclusion: Identifying mechanical predictors for dental injury of unprotected teeth provides a first step in understanding which aspects of an impact event attribute to dental injury and can lay the foundation for future studies that examine alteration in injury mechanics associated with protection devices.
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http://dx.doi.org/10.1111/edt.12645 | DOI Listing |
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