Sentence examples for gender were generated from inspiring English sources

Crude linear regression models stratified by gender were generated with main outcome the BMI (kg m−2) and independent factor, the pasta consumption (grams per day) in both Moli-sani and INHES populations.

Crude and multi-adjusted models stratified by gender were generated with main outcome the BMI (Kg m-2) and independent factor, the predicted pasta-body weight residuals in both Moli-sani and INHES datasets.

Heatmaps, as well as Student's t-test p-values describing the ability of the genes identified by the above method to distinguish gender were generated in R. We wish to extend our thanks to Vishal Saxena (Brigham and Women's Hospital), Ronald E. Vincent (BIOCON SCIENTIFIC), and Enrico Sassi (North Dakota State University) for thoughtful reading of the manuscript.

Permuted block randomization in blocks of four, stratified by gender was generated by an investigator not otherwise involved with study subjects using a computerized randomization program (Dallal 2003).

Later, gender reports were generated from national HRIS data to explore the gender concentration within the medical cadre.

Given that age and gender specific comparative data were generated based on subgroups consisting of n=156 to 542 subjects each, the sample sizes were sufficient to provide normative data.

Normative data for the PHQ-15 in the general population were generated for both genders and different age levels and can be used for the interpretation and comparisons with other populations.

Given that age- and gender-specific comparative data were generated based on subgroups that consisted of 73 to 180 subjects each, the sample sizes were sufficient to provide sound normative data.

Sequence reads were generated from gender specific cDNA libraries on the 454 Life Sciences FLX instrument.

Mean differences in LOS adjusted for age, gender, comorbidities, and injury severity were generated using multivariate linear regression.

Summary statistics were generated with gender, type of provider, type of facility, working status and reasons for leaving as the categorical variable.