Effect of Some Evaluation Methods on the Consequence of Final Exam for Freshman Medical Students

Background & Objective: In Iran, evaluation is one of the most important bases of medical education and one of the best criteria for the categorization of medical universities. Therefore, we aimed to compare some methods of evaluation for medical students. Methods: The target group included all basic level medical students studying medical mycology during the fifth semester. The tested methods of evaluations were: development test, post-test, class reports, and final exam. Three groups of evaluation were compared as a course plan of mycology for nine sessions. Results: There was a significant direct relation between the midterm and final exam, so that the increased midterm and post-test marks caused improvement in the final exam. Moreover, there was a relative correlation between the class reports and final exam. Conclusion: The results of the present study showed that each tested continuous evaluation is able to improve the final exams. Keywords Evaluation Medical students Final exam Midterm Post-tes

Stress monitoring using wearable sensors:a pilot study and stress-predict dataset

With the recent advancements in the field of wearable technologies, the opportunity to monitor stress continuously using different physiological variables has gained significant interest. The early detection of stress can help improve healthcare and minimizes the negative impact of long-term stress. This paper reports outcomes of a pilot study and associated stress-monitoring dataset, named the “Stress-Predict Dataset”, created by collecting physiological signals from healthy subjects using wrist-worn watches with a photoplethysmogram (PPG) sensor. While wearing these watches, 35 healthy volunteers underwent a series of tasks (i.e., Stroop color test, Trier Social Stress Test and Hyperventilation Provocation Test), along with a rest period in-between each task. They also answered questionnaires designed to induce stress levels compatible with daily life. The changes in the blood volume pulse (BVP) and heart rate were recorded by the watch and were labelled as occurring during stress-inducing tasks or a rest period (no stress). Additionally, respiratory rate was estimated using the BVP signal. Statistical models and personalised adaptive reference ranges were used to determine the utility of the proposed stressors and the extracted variables (heart rate and respiratory rate). The analysis showed that the interview session was the most significant stress stimulus, causing a significant variation in heart rate of 27 (77%) participants and respiratory rate of 28 (80%) participants out of 35. The outcomes of this study contribute to the understanding the role of stressors and their association with physiological response and provide a dataset to help develop new wearable solutions for more reliable, valid, and sensitive physio-logical stress monitoring

Characterization of greater middle eastern genetic variation for enhanced disease gene discovery

The Greater Middle East (GME) has been a central hub of human migration and population admixture. The tradition of consanguinity, variably practiced in the Persian Gulf region, North Africa, and Central Asia1-3, has resulted in an elevated burden of recessive disease4. Here we generated a whole-exome GME variome from 1,111 unrelated subjects. We detected substantial diversity and admixture in continental and subregional populations, corresponding to several ancient founder populations with little evidence of bottlenecks. Measured consanguinity rates were an order of magnitude above those in other sampled populations, and the GME population exhibited an increased burden of runs of homozygosity (ROHs) but showed no evidence for reduced burden of deleterious variation due to classically theorized ‘genetic purging’. Applying this database to unsolved recessive conditions in the GME population reduced the number of potential disease-causing variants by four- to sevenfold. These results show variegated genetic architecture in GME populations and support future human genetic discoveries in Mendelian and population genetics

A statistical decision support system incorporating personalised adaptive reference ranges for longitudinal monitoring in prostate cancer

The overall aims of this thesis are to use modern approaches in Biostatistics to help clinicians diagnose Prostate Cancer (PCa) early, and treat effectively, and to help patients choose between treatment options. Biostatistics is both a primary and an enabling discipline, a fundamental requirement of all quantitative research, upon which the validity and integrity of research findings are dependent. This thesis encompasses both aspects. The primary (methodological) component involved the development of new and novel methods for generating adaptive ranges in longitudinal monitoring of biomarkers. The enabling component was to deliver on the requirements outlined by my funders (Prostate Cancer Institute (PCI) in the National University of Ireland, Galway), namely to build a decision support system that i) displays useful summary information of PCI data from several sources and ii) presents the results of several statistical analyses relating to treatment comparisons and outcomes in a manner that was informative to clinicians and patients. The thesis is comprised of three Work Packages (WP). In the first WP, methods to generate personalised adaptive reference ranges (i.e. ranges that adapt and account for an individual\u27s previous data) will be developed that allow clinicians to identify meaningful changes in an individual\u27s blood test results more quickly compared to decision making using conventional normal ranges. Application of biomarker monitoring in elite sports will be presented also. Current techniques involve implementation of a Bayesian approach when the variability within individuals is assumed to be fixed. This thesis will further extend the current literature by accommodating different within individual variability structure that is more realistic, and will result in wider applicability. Additionally, the use of an approximate EM algorithm to produce computationally efficient adaptive ranges for large streaming datasets will also be proposed. A comprehensive simulation study will be undertaken to assess the performance of the methods proposed. The second WP relates to identifying and assessing the main health outcomes following PCa treatment. In particular, the PCa treatment outcomes under different treatment options and based on different risk factors will be compared using suitable statistical methods. Finally, in the third WP a modern statistical decision support system will be developed to enable patients make more informed and reliable decisions about their treatment choices. To conclude, areas of further work across the three WPs will be outlined.2021-12-0

A statistical decision support system incorporating personalised adaptive reference ranges for longitudinal monitoring in prostate cancer

The overall aims of this thesis are to use modern approaches in Biostatistics to help clinicians diagnose Prostate Cancer (PCa) early, and treat effectively, and to help patients choose between treatment options. Biostatistics is both a primary and an enabling discipline, a fundamental requirement of all quantitative research, upon which the validity and integrity of research findings are dependent. This thesis encompasses both aspects. The primary (methodological) component involved the development of new and novel methods for generating adaptive ranges in longitudinal monitoring of biomarkers. The enabling component was to deliver on the requirements outlined by my funders (Prostate Cancer Institute (PCI) in the National University of Ireland, Galway), namely to build a decision support system that i) displays useful summary information of PCI data from several sources and ii) presents the results of several statistical analyses relating to treatment comparisons and outcomes in a manner that was informative to clinicians and patients. The thesis is comprised of three Work Packages (WP). In the first WP, methods to generate personalised adaptive reference ranges (i.e. ranges that adapt and account for an individual's previous data) will be developed that allow clinicians to identify meaningful changes in an individual's blood test results more quickly compared to decision making using conventional normal ranges. Application of biomarker monitoring in elite sports will be presented also. Current techniques involve implementation of a Bayesian approach when the variability within individuals is assumed to be fixed. This thesis will further extend the current literature by accommodating different within individual variability structure that is more realistic, and will result in wider applicability. Additionally, the use of an approximate EM algorithm to produce computationally efficient adaptive ranges for large streaming datasets will also be proposed. A comprehensive simulation study will be undertaken to assess the performance of the methods proposed. The second WP relates to identifying and assessing the main health outcomes following PCa treatment. In particular, the PCa treatment outcomes under different treatment options and based on different risk factors will be compared using suitable statistical methods. Finally, in the third WP a modern statistical decision support system will be developed to enable patients make more informed and reliable decisions about their treatment choices. To conclude, areas of further work across the three WPs will be outlined.2021-12-0

khsos

در مطالعۀ حاضر، علاوه بر مرور ادبیات مربوط به خصوصیسازی آموزش عالی، عملکرد آن با استفادهاز متون نظری و نیز پرسش از رؤسا و مدیران دانشگاهها و مؤسسات آموزش عالی دولتی و غیر دولتی غیر انتفاعی کشور بررسی شده است. نتایج بررسی نشان می دهد که به طور کلی روند خصوصی-سازی آموزش عالی در ایران بسیار شبیه سایر بخش هاست و دارای ویژگ یها ی زی ر است : بدونحضور بخش خصوصی واقعی در کنار نقش گسترده بخش دولتی و شبه دولتی در آن، بدون رقابت ،بدون آزادسازی بازار، بدون چارچوبهای نظارت و ارزیابی کیفیت، بدون حضور مؤسسات خارج ی،بدون حمایتهای مالی و قانونی از بخش خصوص ی واقع ی، بدون مبا نی نظر ی و نی ز استفاده ازخصوصیسازی به عنوان ابزار گسترش آموزش عالی تنها در جهت پاسخگویی به فشارها ی ناش ی ازتقاضای اجتماعی ورود به آموزش عالی. در پایان نیز پیشنهادهایی برای اجرای صحیح خصوصیسازیآموزش عالی در کشور ارائه شده است

Visualising statistical models using dynamic nomograms.

Translational Statistics proposes to promote the use of Statistics within research and improve the communication of statistical findings in an accurate and accessible manner to diverse audiences. When statistical models become more complex, it becomes harder to evaluate the role of explanatory variables on the response. For example, the interpretation and communication of the effect of predictors in regression models where interactions or smoothing splines are included can be challenging. Informative graphical representations of statistical models play a critical translational role; static nomograms are one such useful tool to visualise statistical models. In this paper, we propose the use of dynamic nomogram as a translational tool which can accommodate models of increased complexity. In theory, all models appearing in the literature could be accompanied by the corresponding dynamic nomogram to translate models in an informative manner. The R package presented will facilitate this communication for a variety of linear and non-linear models

A comparison of methods to generate adaptive reference ranges in longitudinal monitoring

In a clinical setting, biomarkers are typically measured and evaluated as biological indicators of a physiological state. Population based reference ranges, known as ‘static’ or ‘normal’ reference ranges, are often used as a tool to classify a biomarker value for an individual as typical or atypical. However, these ranges may not be informative to a particular individual when considering changes in a biomarker over time since each observation is assessed in isolation and against the same reference limits. To allow early detection of unusual physiological changes, adaptation of static reference ranges is required that incorporates within-individual variability of biomarkers arising from longitudinal monitoring in addition to between-individual variability. To overcome this issue, methods for generating individualised reference ranges are proposed within a Bayesian framework which adapts successively whenever a new measurement is recorded for the individual. This new Bayesian approach also allows the within-individual variability to differ for each individual, compared to other less flexible approaches. However, the Bayesian approach usually comes with a high computational cost, especially for individuals with a large number of observations, that diminishes its applicability. This difficulty suggests that a computational approximation may be required. Thus, methods for generating individualised adaptive ranges by the use of a time-efficient approximate Expectation-Maximisation (EM) algorithm will be presented which relies only on a few sufficient statistics at the individual level