Background: Tumors of the oral cavity are under
estimated in general dental and medical practice,
some authors describe it as the forgetting disease,
others wondering if the attention paid to this disease
compared to its fatality (The 5-year survival rate is
about 50%) is enough for disease control? However;
this disease deserves a comprehensive assessment by
all dental and medical fields assumed to examine the
oral cavity regularly, especially otolaryngologist.
Objectives: To find out the sensitivity and specificity
of clinical examination in diagnosing oral tumors and
premalignant conditions by otolaryngologist.
Methods: Across sectional retrospective study was
conducted in the:
-study design: Cross sectional.
-settings: Ear Nose Throat (ENT)departments in
Al-kindy Teaching Hospital
and Al-Yarmouk Teaching Hospital.
On Patients attending ENT department with oral
presentation.
The outcome variables includes: The sensitivity,
specificity, positive predictive value (Pv+ve), negative
predictive value (PV-ve), and the accuracy of clinical
examination in diagnosing oral tumors and
premalignant conditions in ENT clinic.
Results: The results revealed a high sensitivity and
specificity for otolaryngologist in diagnosing
malignant conditions and premalignant lesions of the
oral cavity.
Conclusion: The study highlights the need for fixed
clinical criteria for early diagnosis of premalignant
conditions and oral tumors
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The method binery logistic regression and linear discrimint function of the most important statistical methods used in the classification and prediction when the data of the kind of binery (0,1) you can not use the normal regression therefore resort to binary logistic regression and linear discriminant function in the case of two group in the case of a Multicollinearity problem between the data (the data containing high correlation) It became not possible to use binary logistic regression and linear discriminant function, to solve this problem, we resort to Partial least square regression.
In this, search th
... Show MoreInformation about soil consolidation is essential in geotechnical design. Because of the time and expense involved in performing consolidation tests, equations are required to estimate compression index from soil index properties. Although many empirical equations concerning soil properties have been proposed, such equations may not be appropriate for local situations. The aim of this study is to investigate the consolidation and physical properties of the cohesive soil. Artificial Neural Network (ANN) has been adapted in this investigation to predict the compression index and compression ratio using basic index properties. One hundred and ninety five consolidation results for soils tested at different construction sites
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