Abstract
Background: Academic performance is an important reason for the stress and stress due to poor academic performance further deteriorates the academic performance of the student and it became a vicious cycle. Some predictors can be identified which can help finding the student who can be considered as potentially poor performer for future and such students can be mentored.

Aims and Objectives: This study was designed to predict the scores of pharmacology in final university examination based on the first MBBS score, first internal assessment score, and gender.

Materials and Methods: This record-based study was done at the Department of Pharmacology, GMERS Medical College, Dharpur (Patan), Gujarat. The study was done in two steps - in step one, the prediction equation for total marks in pharmacology in the second MBBS university examination was derived through multiple linear regression on the basis of independent variables (first MBBS total marks, first internal total marks, and gender) and in the second step, the equation so obtained was tested on the second batch of the students by comparing predicted marks with the actually obtained marks in pharmacology in the second M.B.B.S. university examination.

Results: In the first batch, total marks in the 1st year MBBS examination and total marks in the first internal assessment were moderately correlated with the total marks of pharmacology in the 2nd year MBSS (r = 0.605, P = 0.00 and r = 0.589, P = 0.00, respectively) and gender was not significantly contributing to multiple linear regression model for prediction of score. A predictive equation was derived and tested on the second batch of the students. It can be observed that for around 94% of the students’ actual marks lies within plus or minus 10 marks of predicted marks.

Conclusion: Prediction of academic performance from this technique can be a good technique to select the potential poor performers so that these students can be targeted for extra teaching or teaching by different methodology.

Key words: Gender; Pharmacology Score; Prediction; Previous Academic Performance