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.
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Data centric techniques, like data aggregation via modified algorithm based on fuzzy clustering algorithm with voronoi diagram which is called modified Voronoi Fuzzy Clustering Algorithm (VFCA) is presented in this paper. In the modified algorithm, the sensed area divided into number of voronoi cells by applying voronoi diagram, these cells are clustered by a fuzzy C-means method (FCM) to reduce the transmission distance. Then an appropriate cluster head (CH) for each cluster is elected. Three parameters are used for this election process, the energy, distance between CH and its neighbor sensors and packet loss values. Furthermore, data aggregation is employed in each CH to reduce the amount of data transmission which le
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