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Showing 3 results for Estimation
Dr Jafar Hasani, Samaneh Ghooshchian Choobmasjedi , Volume 18, Issue 4 (12-2013)
Abstract
ABSTRACT
Background and Aim: Estimation abilities are used in everyday life to solve problems for which the answers are not readily available. The goal of this study was to assess cognitive estimation in patients with major depressive disorder (MDD), patients with Alzheimer’s disease (AD) and normal individuals.
Method and Materials: In this study we used convenience sampling method. Diagnostic and inclusion criteria were determined. The subjects were assigned to three groups including patients with major depressive disorder (MDD), patients with Alzheimer’s disease (AD) and normal individuals (n=25).We assessed the subjects by Cognitive Estimation Test (CET). Data was analyzed by multivariate analysis of variance (MANOVA).
Results: Our results showed that both patient groups had impaired cognitive estimation in all dimensions and higher total scores in comparison to normal individuals. Alzheimer patients showed a high performance impairment in all cognitive estimation dimensions and had higher total scores compared to the patients with major depressive disorder.
Conclusions: The findings of this study supported impairments in general knowledge and other executive functions in patients with major depressive disorder (MDD) and patients with Alzheimer’s disease (AD).
Key words: Cognitive estimation, Alzheimer’s disease (AD), Major depressive disorder (MDD), Executive functions.
Received: June 22, 2013 Accepted: Dec 7, 2013
Conflict of interest: None declared
Mahan Bahmanziari, Amal Saki Malehi, Maedeh Raesizadeh, Mohammad Seghatoleslami, Mr Mehran Hoseinzade, Elham Maraghi, Volume 26, Issue 6 (12-2021)
Abstract
Background and Aim: Breast cancer is the most important cause of cancer death in women. The purpose of this study was to evaluate the effect of Estrogen Receptor (ER), Human Epidermal Growth Receptor (HER2) and other factors on post-surgical survival of the patients with breast cancer using Bayesian approach for parametric proportional hazards model.
Materials and Methods: This was a retrospective study. Data of 165 breast cancer patients who had undergone surgery at Ahvaz Healing Diagnostic Center from 2004 to 2014 were recorded in a data collection form. The variables of age, tumor size, number of lymph nodes involved, cancer grade, ER status and HER2 status were evaluated. Survival time was calculated from the date of surgery to the date of death or study end date (September 2015), in months. In the Bayesian approach in parametric survival analysis models with proportional hazards, the lateral distribution of parameters was estimated using MCMC method. Also, we evaluated efficiency of the models using the deviance information criterion. All data analysis steps were performed by using Stata15 software. Significance coefficients of the model were determined using the 95% credible interval.
Results: The mean and standard deviation of age were 46.40 and 9.94 years, respectively. Deviance information criterion for Weibull parametric model was lower than those of other parametric models. Based on the Bayesian estimation of the Weibull's proportional hazards parametric model, tumor size (HR = 1.40), the number of involved lymph nodes (HR = 1.016), Ki67 status (HR = 1.115), tumor grade (HR = 1.022), HER2 status (HR = 1.760) and ER status (HR = 1.381) had a positive effect on risk of death. Age had a negative effect on risk of death (HR=0.978).
Conclusion: Based on the Bayesian proportional hazards Weibull model, tumor size, the number of involved lymph nodes, Ki67, tumor's grade, HER2 and ER had a positive effect on the risk of death.
Dr Bahram Mohaghegh, Dr Niusha Shahidi Sadeghi, Mr Amir Mohamadi, Mr Mohamad Nazari, Dr Mohamad Hosein Salarianzadeh, Dr Amir Ahamad Akhavan, Dr Mohamad Reza Maleki, Dr Mostafa Deilami, Mrs Azam Sadat Hoseini, Dr Fatemeh Talebi, Mr Ali Akbar Abedi, Mrs Maasumeh Faraj Allahi, Volume 29, Issue 4 (10-2024)
Abstract
Background and Aim: Human resource planning of faculties in medical sciences universities is an important requirement to achieve the function of producing health workforce. This study aimed to develop a model for the estimation of faculties in universities of medical sciences.
Materials and Methods: In this practical study, factors influencing the workload of faculty members were identified by quantitative and qualitative method. Data collection was done by document review, expert group interviews and curriculum analysis. Approaches based on curricula and the requirements of specialized boards were used to develop the model. Data entry and analysis were done in Microsoft Excel 2010.
Results: To estimate the faculty members required for specialized, sub-specialized and fellowship courses of medicine, linear model consisting of the combined variable of the number of annual student admissions multiplied by the academic staff coefficient and the variable of the fixed number of faculty for each course, as well as the specialties required separately for each course, was constructed. The needed faculties for other courses were according to the other model consists of the independent variable of the number of students and a combined variable namely "faculty member coefficient", constructed of parameters including the number of credit hours obligated to be taught by a faculty member per semester, the optimum number of students in the class, the number of majors' semesters and the course's adjusted value.
Conclusion: This model is an innovative method to estimate the needed faculties in Iranian universities of medical sciences that could raise the efficiency of health workforce's production.
Key words: Faculty member, Model, Estimation, Medical Sciences University.
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