Back ground : Fever is a common medical problem in
children. parents have been shown unrealistic fears of
harmful effects of fever in their children. Resulting in
inappropriate management of fever in their children.
Objective: the objective of this study was to survey
parents about their knowledge andattitude concerning fever
in their children.
Methods : The study involved random selection of
parents who brought their febrile children to emergency
department or out-patient clinics of five teaching and non
teaching hospitals in Baghdad from first of October to end
of December 2002.
Parents of 400 febrile children were interviewed using a
standard questionnaire to obtain sociodemographic
information and current knowledge of fever.
Results: Approximately 69% of the respondents were
female, 68% their age was in the range of 21-35 years.
Seventy seven percent of parents had two or more children.
More than 70% of parents demonstrated a poor
understanding of definition of fever , maximum
temperature of untreated fever and threshold temperature
requiring antipyretics . Twenty sevev percent of parents
considered temperature less than 38.0 c
o
to be fever,
another 27% did not know, the definition of fever, 61% felt
that temperature of less than 40,0 c
o
could be dangerous to
a child, and 27% could not define high fever. Another 28%
believed that if left untreated, temperature could rise to
42.0 c
o
or higher, but 39% could not provide an answer,
and about 60% did not know the minimum temperature for
administering antipyretics. And 60% did not know the
minimum temperature for bathing , or sponging.
Approximately 93% of parents demonstrated fear of
consequent body damage from fever , including convulsion
,brain damage ,coma, blindness and even death.
Conclusion: parental misconception about fever reflects
the lack of active health education. Health professionals
have apparently not done enough to educate parents on this
common medical problem and it,
s consequences
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The
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Thyroid disease is a common disease affecting millions worldwide. Early diagnosis and treatment of thyroid disease can help prevent more serious complications and improve long-term health outcomes. However, thyroid disease diagnosis can be challenging due to its variable symptoms and limited diagnostic tests. By processing enormous amounts of data and seeing trends that may not be immediately evident to human doctors, Machine Learning (ML) algorithms may be capable of increasing the accuracy with which thyroid disease is diagnosed. This study seeks to discover the most recent ML-based and data-driven developments and strategies for diagnosing thyroid disease while considering the challenges associated with imbalanced data in thyroid dise
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