Background: Laparoscopic cholecystectomy
has become the standard of care for the
elective management of cholelithiasis. Little
information exists, however, regarding the
appropriateness of this procedure in the setting
of acute symptomatology.
Objective: This study was designed to
evaluate the outcome of laparoscopic
cholecystectomy in acute and severe acute
cholecystitis based on early and late biliary
complications, their incidence and
management, and conversion rates to open
surgery.
Methods: A prospective study done between
April 2007 and November 2010, in the
department of general surgery, medical city
teaching hospital, Baghdad. Includes patients
with acute cholecystitis admitted for
laparoscopic cholecystectomy; they were
divided into two groups, (group 1) including
patients with acute cholecystitis; (group 2)
including patients with severe acute
cholecystitis.
Results: 306 patients were admitted for
laparoscopic cholecystectomy, 71 (23.2%) of
them with acute cholecystitis and was involved
in this study; they were divided into two
groups, (group 1) patients with acute
cholecystitis 61(85.9%), (group 2) patients
with severe acute cholecystitis 10 (14%);
including gangrenous gallbladder 3(30%), and
empyematous gallbladder 7(70%). Patients in
group 2 were significantly older than in group
1. Female sex was more significant in group 1,
while male sex was more significant in group.
There was no procedure related mortality.
Conclusion: laparoscopic cholecystectomy
for acute cholecystitis is safe and associated
with a low morbidity, mortality, and a low
conversion rate.
The importance of forecasting has emerged in the economic field in order to achieve economic growth, as forecasting is one of the important topics in the analysis of time series, and accurate forecasting of time series is one of the most important challenges in which we seek to make the best decision. The aim of the research is to suggest the use of hybrid models for forecasting the daily crude oil prices as the hybrid model consists of integrating the linear component, which represents Box Jenkins models and the non-linear component, which represents one of the methods of artificial intelligence, which is long short term memory (LSTM) and the gated recurrent unit (GRU) which represents deep learning models. It was found that the proposed h
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