Background: Bowel preparation prior to
colonic surgery usually includes antibiotic
therapy together with mechanical bowel
preparation which may cause discomfort to the
patients, prolonged hospitalization and water
& electrolyte imbalance.
Objective: to assess whether elective colon
and rectal surgery may be safely performed
without preoperative mechanical bowel
preparation.
Method: the study includes all patients who
had elective large bowel resection at Medical
City – Baghdad Teaching Hospital between
Feb, 2007 to Jan, 2010. Emergency operations
were not included. The patients were randomly
assigned to the 2 study groups (with or without
mechanical bowel preparation.
Results: A total of 165 patients participated
in the study, 82 with mechanical bowel
preparation and 83 without. The 2 groups
were similar in age, sex and type of surgical
procedure. 134 patients (81.2 %) underwent
surgery owing to colorectal cancer & 31
patients (18.8 %) owing to benign disease.
The hospitalization period was longer in the
bowel-prepared group (mean ± SD, 8.2 ± 5.1
days) as compared with the non prepared
group (mean ± SD, 8.0 ± 2.7 days). However,
this difference was not statistically significant.
The time until the 1st bowel movement was
similar between the 2 groups : a mean ± SD of
4.2 ± 1.3 days in the non prepared group as
compared with a men ± SD 4.3 ± 1.1 days in
the prepared group ( P = NS ).
Conclusion: Our results suggest that no
advantage is gained by preoperative
mechanical bowel preparation in elective
colorectal surgery.
S Khalifa E, N Adil A, AS Mazin M…, 2008
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KE Sharquie, JR Al-Rawi, AA Noaimi, RA Al-Khammasi, Iraqi Journal of Community Medicine, 2018
Multiple linear regressions are concerned with studying and analyzing the relationship between the dependent variable and a set of explanatory variables. From this relationship the values of variables are predicted. In this paper the multiple linear regression model and three covariates were studied in the presence of the problem of auto-correlation of errors when the random error distributed the distribution of exponential. Three methods were compared (general least squares, M robust, and Laplace robust method). We have employed the simulation studies and calculated the statistical standard mean squares error with sample sizes (15, 30, 60, 100). Further we applied the best method on the real experiment data representing the varieties of
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In this paper new methods were presented based on technique of differences which is the difference- based modified jackknifed generalized ridge regression estimator(DMJGR) and difference-based generalized jackknifed ridge regression estimator(DGJR), in estimating the parameters of linear part of the partially linear model. As for the nonlinear part represented by the nonparametric function, it was estimated using Nadaraya Watson smoother. The partially linear model was compared using these proposed methods with other estimators based on differencing technique through the MSE comparison criterion in simulation study.
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This study focuses on evaluating the suitability of three interpolation methods in terms of their accuracy at climate data for some provinces of south of Iraq. Two data sets of maximum and minimum temperature in February 2008 from nine meteorological stations located in the south of Iraq using three interpolation methods. ArcGIS is used to produce the spatially distributed temperature data by using IDW, ordinary kriging, and spline. Four statistical methods are applied to analyze the results obtained from three interpolation methods. These methods are RMSE, RMSE as a percentage of the mean, Model efficiency (E) and Bias, which showed that the ordinary krigingis the best for this data from other methods by the results that have b
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