Sentence examples for gender visits from inspiring English sources

Logistic regression analyses evaluated the influence of age, gender, visits per year, severity of AI, and VAS score on the dependent variables of OHRQoL, dental fear, and dental beliefs.

Using Chi-square tests, response-patterns were compared across age, gender, visit frequency, number of people in the group, or whether walking a dog.

The medical information contained in the NHIRD included patient's birth date, gender, visit date, hospitalization date, medical services providers, treatments, managements, and the reasons for visits or hospitalization.

Gender, age, visiting frequency, and visiting time were all related to patient satisfaction.

In order to compare scores between categories (gender, GP visits, perceived severity), non-parametric tests were used (Wilcoxon rank sum two sample test for comparison between two groups and Kruskal-Wallis test for comparison between more than two groups).

In multiple regression analysis of the relationship between death from heart infarction as a dependent variable and CI as independent variable with controlling for age, gender, dental visits, dental plaque, periodontal pockets, education, income, socioeconomic status, and pack-years of smoking, CI score appeared to be associated with 2.3 times the odds ratio for cardiac death.

Secondary efficacy outcomes for the PANSS total and subscores were analyzed using the mixed-model repeated measures (analysis) model (MMRM), with the fixed categorical effects of treatment, gender, investigator, visit, and treatment-by-visit interaction, as well as the continuous, fixed covariate of baseline score and baseline-by-visit interaction.

Analysis of treatment difference for total daily insulin dose was assessed using a repeated-measures analysis model with terms for treatment, country, gender, time (visit) and treatment-by-time (visit).

In the univariate analysis, age was related to all domains except visiting; gender to comfort, visiting, and intimacy; level of education to comfort and cleanliness; marital status to information, human care, intimacy, and cleanliness; length of hospital stay to visiting and cleanliness, and previous admissions to human care, comfort, and cleanliness.

Descriptive statistics were calculated to describe demographic information such as age and gender, initial visit status, and encounter department.

Analyses were adjusted for continuous patient age, patient race/ethnicity, physician specialty, and stratified by patient gender and visit setting.