The validity of Iran's national university entrance examination (Konkoor) for predicting medical students' academic performance.

BACKGROUND: In Iran, admission to medical school is based solely on the results of the highly competitive, nationwide Konkoor examination. This paper examines the predictive validity of Konkoor scores, alone and in combination with high school grade point averages (hsGPAs), for the academic performance of public medical school students in Iran. METHODS: This study followed the cohort of 2003 matriculants at public medical schools in Iran from entrance through internship. The predictor variables were Konkoor total and subsection scores and hsGPAs. The outcome variables were (1) Comprehensive Basic Sciences Exam (CBSE) scores; (2) Comprehensive Pre-Internship Exam (CPIE) scores; and (3) medical school grade point averages (msGPAs) for the courses taken before internship. Pearson correlation and regression analyses were used to assess the relationships between the selection criteria and academic performance. RESULTS: There were 2126 matriculants (1374 women and 752 men) in 2003. Among the outcome variables, the CBSE had the strongest association with the Konkoor total score (r = 0.473), followed by msGPA (r = 0.339) and the CPIE (r = 0.326). While adding hsGPAs to the Konkoor total score almost doubled the power to predict msGPAs (R2 = 0.225), it did not have a substantial effect on CBSE or CPIE prediction. CONCLUSIONS: The Konkoor alone, and even in combination with hsGPA, is a relatively poor predictor of medical students' academic performance, and its predictive validity declines over the academic years of medical school. Care should be taken to develop comprehensive admissions criteria, covering both cognitive and non-cognitive factors, to identify the best applicants to become "good doctors" in the future. The findings of this study can be helpful for policy makers in the medical education field.

Varying Manpower Alters Dental Health in a Developing Health Care System.

OBJECTIVES: This study assessed relationships between oral health care workforce and dental health in 12-year-olds in a developing health care system in Iran from 1992 to 2014 and compared these findings with the most recent corresponding findings in selected countries. METHODS: Data regarding oral health care workers from 1962 to 2014 were extracted from the comprehensive human resource data bank of the Shahid Beheshti Research Institute of Dental Sciences. Data regarding decayed, missing, and filled permanent teeth (DMFT) of 12-year-olds, extracted from official statistics, described dental health. Comparisons with other countries utilised the database of the World Health Organization. Changes in the DMFT index with fluctuations in the number of oral health care workers were investigated using exploratory data analysis methods. Associations of DMFT with the density of the oral health care workforce were evaluated using a multiple linear regression model. RESULTS: The trend in supply of dental workforce in Iran began to expand in the 1970s and, after a reduction in 2003 to 2007, reached a peak by 2014. Means of DMFT indices of 12-year-olds in Iran fluctuated between 1.50 and 2.40 from 1992 to 2014. The relationship between the dentist to population ratio and the DMFT index of 12-year-olds showed a downwards trend (r = -0.994; P < .001) until 1998 and afterwards an upwards trend (r = 0.887; P < .001). Globally, the DMFT index decreased in countries with a preventively-oriented oral health care workforce. CONCLUSIONS: Increased numbers of dentists have no significant impact on improving dental health in 12-year-olds. To promote dental health, the system providing health services should implement a preventively-oriented approach when planning for the oral health workforce.

Computational Modeling and Analysis to Predict Intracellular Parasite Epitope Characteristics Using Random Forest Technique.

BACKGROUND: In a new approach, computational methods are used to design and evaluate the vaccine. The aim of the current study was to develop a computational tool to predict epitope candidate vaccines to be tested in experimental models. METHODS: This study was conducted in the School of Allied Medical Sciences, and Center for Research and Training in Skin Diseases and Leprosy, Tehran University of Medical Sciences, Tehran, Iran in 2018. The random forest which is a classifier method was used to design computer-based tool to predict immunogenic peptides. Data was used to check the collected information from the IEDB, UniProt, and AAindex database. Overall, 1,264 collected data were used and divided into three parts; 70% of the data was used to train, 15% to validate and 15% to test the model. Five-fold cross-validation was used to find optimal hyper parameters of the model. Common performance metrics were used to evaluate the developed model. RESULTS: Twenty seven features were identified as more important using RF predictor model and were used to predict the class of peptides. The RF model improves the performance of predictor model in comparison with the other predictor models (AUC±SE: 0.925±0.029). Using the developed RF model helps to identify the most likely epitopes for further experimental studies. CONCLUSION: The current developed random forest model is able to more accurately predict the immunogenic peptides of intracellular parasites.