scholarly journals INFLUENCIA DE LAS VARIABLES DE ADMISIÓN SOBRE EL ÉXITO ACADÉMICO EN EL PRIMER AÑO DE LA CARRERA DE LICENCIATURA EN MEDICINA Y CIRUGÍA DE UCIMED

2016 ◽  
Vol 7 (1) ◽  
pp. 301-320
Author(s):  
Hilda María Sancho-Ugalde ◽  
Juan Carlos Vanegas-Pissa
Keyword(s):  
Logistic Regression ◽  
Academic Success ◽  
Predictive Ability ◽  
Target Population ◽  
Admission Criteria ◽  
First Semester ◽  
General Test ◽  
Admissions Process ◽  
Economic Measure ◽  
Predictors Of Academic Success

El presente trabajo tiene como objetivo evaluar la capacidad predictiva de las variables sociodemográficas y del proceso de admisión como factores que incidan sobre el éxito académico de los estudiantes que cursan el primer semestre de la carrera de Licenciatura en Medicina y Cirugía, de la Universidad de Ciencias Médicas (UCIMED), Costa Rica. La población objetivo está integrada por los alumnos de nuevo ingreso de las cohortes 2009 a 2013 inclusive, (N=1558 estudiantes). Se definió el éxito académico como la aprobación del total de cursos del primer semestre de la carrera; y mediante la estadística descriptiva y la técnica multivariada de Regresión Logística, se determinó la incidencia que tienen los diferentes factores, de índole socioeconómica y propios del proceso de admisión como predictoras del éxito académico. Los resultados permiten concluir que las variables significativas del éxito académico son las calificaciones en la prueba de conocimientos y el promedio de los dos últimos años de secundaria y haber tomado la decisión de estudiar con un mayor tiempo de reflexión.Palabras clave: Éxito Académico; Criterios de Admisión; Regresión Logística; Indicadores de Ingreso; Estudiantes de MedicinaAbstractThis study aims to assess the predictive ability of the sociodemography variables and the admissions process, as factors that affect the academic success of students in the first semester of the bachelor of medicines and surgery. The target population is composed of 2009-2013 freshmen cohorts (N 1558 students) Academic success as the approval of all courses in the first half of career was defined; and using descriptive statistics and multivariate logistic regression technique, the incidence that different factors, socio-economic measure, and the process of admission itself as predictors of academic success were determined. The results suggest that the significant variables of academic success are the scores on the admission general test and the average of the last two years of high school.Keywords: Academic Success; Admission criteria; Logistic Regression; Indicators of Income; Medical students.

Self-Efficacy and Commitment to College as Predictors of Academic Success

2007 ◽  
Author(s):  
Eric J. Cooley ◽  
Tamina Toray ◽  
Lauren Roscoe ◽  
Morgan Hutmacher ◽  
Amanda Miles
Keyword(s):  
Academic Success ◽  
Self Efficacy ◽  
Predictors Of Academic Success

scholarly journals Evaluation of factors that predict the success rate of trial of labor after the cesarean section

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Mi ◽  
Pengfei Qu ◽  
Na Guo ◽  
Ruimiao Bai ◽  
Jiayi Gao ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Cesarean Section ◽  
Receiver Operating Characteristic Curve ◽  
Success Rate ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Predictive Ability ◽  
Training Set ◽  
History Of ◽  
Operating Characteristic Curve

Abstract Background For most women who have had a previous cesarean section, vaginal birth after cesarean section (VBAC) is a reasonable and safe choice, but which will increase the risk of adverse outcomes such as uterine rupture. In order to reduce the risk, we evaluated the factors that may affect VBAC and and established a model for predicting the success rate of trial of the labor after cesarean section (TOLAC). Methods All patients who gave birth at Northwest Women’s and Children’s Hospital from January 2016 to December 2018, had a history of cesarean section and voluntarily chose the TOLAC were recruited. Among them, 80% of the population was randomly assigned to the training set, while the remaining 20% were assigned to the external validation set. In the training set, univariate and multivariate logistic regression models were used to identify indicators related to successful TOLAC. A nomogram was constructed based on the results of multiple logistic regression analysis, and the selected variables included in the nomogram were used to predict the probability of successfully obtaining TOLAC. The area under the receiver operating characteristic curve was used to judge the predictive ability of the model. Results A total of 778 pregnant women were included in this study. Among them, 595 (76.48%) successfully underwent TOLAC, whereas 183 (23.52%) failed and switched to cesarean section. In multi-factor logistic regression, parity = 1, pre-pregnancy BMI < 24 kg/m2, cervical score ≥ 5, a history of previous vaginal delivery and neonatal birthweight < 3300 g were associated with the success of TOLAC. The area under the receiver operating characteristic curve in the prediction and validation models was 0.815 (95% CI: 0.762–0.854) and 0.730 (95% CI: 0.652–0.808), respectively, indicating that the nomogram prediction model had medium discriminative power. Conclusion The TOLAC was useful to reducing the cesarean section rate. Being primiparous, not overweight or obese, having a cervical score ≥ 5, a history of previous vaginal delivery or neonatal birthweight < 3300 g were protective indicators. In this study, the validated model had an approving predictive ability.

Predictors of thermal increase in magnetic resonance–guided focused ultrasound treatment for essential tremor: histogram analysis of skull density ratio values for 1024 elements

2021 ◽  
pp. 1-6
Author(s):  
Ken Iijima ◽  
Hajime Yokota ◽  
Toshio Yamaguchi ◽  
Masayuki Nakano ◽  
Takahiro Ouchi ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Operating Characteristic ◽  
Focused Ultrasound ◽  
Characteristic Curve ◽  
Density Ratio ◽  
Predictive Ability ◽  
Curve Analysis ◽  
Regression Analyses ◽  
Multivariate Logistic Regression ◽  
Operating Characteristic Curve

OBJECTIVE Sufficient thermal increase capable of generating thermocoagulation is indispensable for an effective clinical outcome in patients undergoing magnetic resonance–guided focused ultrasound (MRgFUS). The skull density ratio (SDR) is one of the most dominant predictors of thermal increase prior to treatment. However, users currently rely only on the average SDR value (SDRmean) as a screening criterion, although some patients with low SDRmean values can achieve sufficient thermal increase. The present study aimed to examine the numerical distribution of SDR values across 1024 elements to identify more precise predictors of thermal increase during MRgFUS. METHODS The authors retrospectively analyzed the correlations between the skull parameters and the maximum temperature achieved during unilateral ventral intermediate nucleus thalamotomy with MRgFUS in a cohort of 55 patients. In addition, the numerical distribution of SDR values was quantified across 1024 elements by using the skewness, kurtosis, entropy, and uniformity of the SDR histogram. Next, the authors evaluated the correlation between the aforementioned indices and a peak temperature > 55°C by using univariate and multivariate logistic regression analyses. Receiver operating characteristic curve analysis was performed to compare the predictive ability of the indices. The diagnostic performance of significant factors was also assessed. RESULTS The SDR skewness (SDRskewness) was identified as a significant predictor of thermal increase in the univariate and multivariate logistic regression analyses (p < 0.001, p = 0.013). Moreover, the receiver operating characteristic curve analysis indicated that the SDRskewness exhibited a better predictive ability than the SDRmean, with area under the curve values of 0.847 and 0.784, respectively. CONCLUSIONS The SDRskewness is a more accurate predictor of thermal increase than the conventional SDRmean. The authors suggest setting the SDRskewness cutoff value to 0.68. SDRskewness may allow for the inclusion of treatable patients with essential tremor who would have been screened out based on the SDRmean exclusion criterion.

scholarly journals Investigation of Factors Affecting the Achievement of University Students with Logistic Regression Analysis: School of Physical Education and Sport Example

SAGE Open ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 215824402090208
Author(s):  
Yeliz Eratlı Şirin ◽  
Mustafa Şahin
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Academic Success ◽  
Physical Education ◽  
University Students ◽  
Logistic Regression Analysis ◽  
Factors Affecting ◽  
Course Success ◽  
Effective Factor ◽  
The Way

In this study, the factors affecting the success of university students were analyzed by logistic regression analysis. In the study, success variable was defined according to the survey information applied to 360 university students studying in School of Physical Education and Sport in Çukurova University and Kahramanmaraş Sütçü İmam University in Turkey, in 2017–2018 academic year. The relationship between the answers to the Likert-type scale questions affecting success variables and the course success was estimated by logistic regression analysis. According to the results of the research, because independent variables such as mother’s education status, age, and class were statistically insignificant, they were not included in the multivariate model. According to the findings, variables such as gender, the university they studied, the way they chose their department, and father’s education are seen as important in the growth of students’ academic success. In addition to this, the variables such as counseling about their profession, support of department’s instructors, and communication with instructor have been found to be considerably effective on success. It was observed that the way they chose their department (willingly–compulsorily) was the most effective factor, and father’s education was the second effective factor. As a result, the success levels of the students were found to differ according to the sociodemographic characteristics and their relations with the instructors. On the contrary, as the instructors’ guidance, support, and communication skills are effective contributors on student’s success, it has been concluded that instructors should take these factors into account.

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