Sentence examples for continuous gender from inspiring English sources
These analyses included in the initial full model age (continuous), gender, ethnicity, and sexual exposure.
The conventional model included age (continuous), gender, family history of diabetes (yes or no) and BMI (continuous).
The logistic model controlled for age (continuous), gender, income, residence, physical activity/inactivity and intake of fruit and vegetables.
Controlled for age (continuous), gender (Male, Female), ethnicity (Aboriginal, White, Other), need level (High, Moderate), and marital status (Single, Other).
Analyses were performed with and without adjusting for a priori selected covariates age (continuous), gender, and average monthly income (continuous) as an indicator of neighborhood socio-economic status.
Age (continuous), gender, years of education (continuous), somatization (continuous), high handgrip forces and using vibrating tools were included in the logistic regression models as confounders.
We also evaluated potential confounding factors identified previously [5, 6, 8, 12 14]: in particular age (continuous variable), gender, education level, smoking status, physical activity, body mass index, chronic musculoskeletal complaints, systolic blood pressure (SBP) and anxiety and depression measured by total HADS score (continuous variable).
For each patient who received a GON injection, information on age (measured as a continuous variable), gender, headache diagnosis, date of first visit, time from first visit to injection, site of injection, past and current medication, effects of injection and eventual follow-up treatment was collected using a standardized pro forma.
The initial full model was adjusted for age (continuous) and gender.
These data are shown in Table 3. aErosion score >0 at enrollment; bPatients with HLA-DRB1 SE, plus either anti-CCP or RF positive; c Adjusted by age at RA onset (continuous) + Gender + Disease duration (≥2 years versus <2 years) + rheumatoid nodules + smoking (ever versus never) + methotrexate (ever versus never).
We also fitted logistic regression models including age (continuous variable), gender and educational level.
