Background:-The Modified Alvarado Scoring System (MASS) has been reported to be a cheap and quick diagnostic tool in patients with acute appendicitis. However, differences in diagnostic accuracy have been observed if the scores were applied to various populations and clinical settings.
Objectives:- The purpose of this study was to evaluate the diagnostic value of Modified Alvarado Scoring System in patients with acute appendicitis in our setting.
Methods:-one hundre twenty eight patients ,were included in this study, admitted to Al-Kindy teaching hospital from June 2009 to June 2010. Patients’ age ranged from 8 to 56 years (21±10) they were divided into three groups; paediatrics, child bearing age females & adult males,. MASS was calculated for each patient included as the diagnosis & treatment were done on the bases of surgeon's clinical decision,confirmation was done by histopathological examination. Finally statistics done included negative appendectomy rate, sensitivity,specificity,positive predictive value,negative predictive value & accuracy.
Results:- Our negative appendectomy rate was 19.5% (22.22% for paediatrics 40.9% for females 4.2% for males). MASS showed sensitivity of 61%(92.8% for paediatrics 38% for females & 58% for males), specificity 80% (75% for paediatrics 88% for females & 50% for males), positive predictive value 92%(92.8% for paediatrics 83% for females 50% for males), negative predictive value 33% ( 75%for paediatrics 50% for females 5% for males) & accuracy 65% ( 88.9% for paediatrics 59% for females 58% for males).
Conclusion:- MASS was of limited help to junior doctors in our setting,clinical assessment & experience are still the gold standard for acute appendicitis.
Objective. Glass-ionomer and resin-modified glass-ionomer cements are versatile materials with the ability to form a direct bond with tooth tissues. The aim of this study was to formulate a novel class of dental bio-interactive restorative material (pRMGIC) based on resin-modified glass-ionomer cements via the inclusion of an organophosphorus monomer, ethylene glycol methacrylate phosphate, with a potential to improve the mechanical properties and also function as a reparative restorative material. Methods. pRMGIC was formulated with modification of the resin phase by forming mixes of ethylene glycol methacrylate phosphate (EGMP; 0–40%wt) and 2-hydroxyethyl methacrylate monomer into the liquid phase of a RMGIC (Fuji II LC, GC Corp.).
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A theoretical calculation of the binding and excitation energies have been used at low – lying energies based on shell model and quantum theory. In this model, we evaluated the energies under assume Ni 28 56 30 as inert core with two nucleon extra, nucleons in the 2P3/2 , 1f 5/2 and 2P1/2 configuration. Modified Surface Delta Interaction (MSDI) and Reid's Potential (RP) theory for two body matrix elements are evaluated by using a Matlab program to calculate the energies of experimental and Reid single particle energies. Our results of the theoretical calculation have been compared with the experimental results, which show no good agreement with the experiment but have a good agreement wit
... Show MoreBackground: Schneiderian first rank symptoms are
considered highly valuable in the diagnosis of
schneideria.
They are more evident in the acute phase of the
disorder and fading gradually with time. Many studies
have shown that the rate of these symptoms are
variable in different countries and are colored by
cultural beliefs and values.
Objectives: To find out the rate of Schneiderian first
rank symptoms among newly diagnosed schizophrenic
patients, to assess which symptom(s) might
predominate in those patients, and to find out if there
is/are any correlation(s) between the occurrence of
these symptoms and the sex of the patients.
Methods: Out of twenty-four patients with no past
psychiatric hi
The interests toward developing accurate automatic face emotion recognition methodologies are growing vastly, and it is still one of an ever growing research field in the region of computer vision, artificial intelligent and automation. However, there is a challenge to build an automated system which equals human ability to recognize facial emotion because of the lack of an effective facial feature descriptor and the difficulty of choosing proper classification method. In this paper, a geometric based feature vector has been proposed. For the classification purpose, three different types of classification methods are tested: statistical, artificial neural network (NN) and Support Vector Machine (SVM). A modified K-Means clustering algorithm
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