Objective To examine the validity of selected entry level characteristics in relation to the GCE A/L examination as independent predictors of performance of students in medical school.Methods A retrospective, analytical study was done at the Faculty of Medicine, University of Kelaniya. Student characteristics at entry were described by sex, the average z-score, General English grade and attempt at GCE A/L examination, and average mark obtained at the English placement test on registration to medical school.used as indicators of performance in medical school. Multivariate analysis using multiple linear regression was carried out using these three outcome measures in relation to four entry point variables as predictors of performance in medical school. Causal path diagrams were constructed using standardised regression coefficients for the whole group and for male and female students separately. Paperspath coefficient with performance at the First Examination than the A/L z-score, as did the English marks. Separate path analyses for male and female students showed that while the significance of the relationships remained the same, the magnitude of the correlation was different.Conclusions Students who gain admission on their 3rd attempt at the AL examination fare much worse than those admitted to medical school on their 1st attempt. Differences between sexes in examination performance are probably linked to both A/L attempt and English language proficiency.

test pool where the rest of them (80%) were considered as the trained data. Results: The final results showed that the artificial neural network achieves an R 2 ¼82%, while ANFIS succeeded to a higher correlation of about 85%, as the estimation markers of correlation between observed and predicted hemodialysis chance in methanol poisoned patients. This shows that, as an artificial model ANFIS is more reliable than the artificial neural network for predicting hemodialysis in methanol poisoning.(Figures-1,2) Conclusions: Artificial intelligent model ANFIS is a better predictor of hemodialysis chance in methanol poisoning, when compared with artificial neural network model.

Results: The number of patients diagnosed with acute renal failure in 7 months was 51 patients. The sex ratio was 2.5 and the mean age was 39.92 AE 26.50 years, with a gap between 4 months and 91 years. Mean serum creatinine was 32.86 AE 24.4 mg / l and hemoglobin 7.45 g / l AE 3.15 g / l. There were 7 patients (13.72%) who underwent hemodialysis and the indications were acute pulmonary oedema and disorders of consciousness. Infections were the main causes of AKI (24 cases or 47.05%). Other causes of AKI were cardio-renal syndrome (8 cases or 15.68%), anemia (7 cases or 13.75%), hepatorenal syndrome (6 cases or 11.76%), AKI postoperative (4 cases). 7.84%) andtaking NSAIDs (2 cases or 3.92%). We report 5 deaths among these AKI. Conclusions: The AKI affects all ages and causes are diverse, but infections are a priority. Serum creatinine was not an indication for hemodialysis. seven patients were hemodialyzed, we would to have a work on AKI in infections in Congo Brazzaville.

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