Background: 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 history and whom were diagnosed as
Schizophrenia for their first time depending on
Diagnostic and Statistical Manual-4th Edition-Text
Revised criteria for diagnosis were evaluated for the
presence of Schneiderian First Rank Symptoms by
using a semi-structured interview schedule.
Results: Out of twenty -three patients (54.7%) had
present with one or more Schneiderian First Rank
Symptoms.' Third person Hallucinatory Voices",
"running Commentary Hallucinatory Voices', and "
Somatic Passivity" were present more frequently than
other symptoms.
The study revealed no sex differences in regard of the
occurrences of the Schneiderian (FRS). More than 82%
of those who had the symptoms showed more than one
symptom.
Conclusions: Many factors influence the presence or
absence of Schneiderian First Rank Symptoms among
schizophrenic patients including the criteria selected
for the diagnosis of the disorder, the tools adopted for
the detection of these symptoms, the duration of the
illness, and probably patient's cultural background.
Although there are individual differences of First Rank
Symptoms among different cultures, still we expect
certain symptoms to be present more than others. The
influence of cultural factors in altering the basic
symptoms of psychiatric illnesses is of great
importance
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Abstract
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Is there a significant effect of consumption culture on marketing deception? &n
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Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att
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