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Multiple logistic regressions were used to compute the odds ratio (OR) for each action and for the bundle compliance adjusted for type of sepsis (severe sepsis or septic shock), SAPS II, presence of comorbidities (any present or none), type of hospital (community vs university) and type of ICU (medical vs mixed).
Adjusted models included age, education (high school or vocational training vs university) and medical training (yes or no).
Data were collected on gender, age, ethnicity (categorised as white vs other ethnicity), marital status (categorised as married/cohabiting vs single/divorced/separated/widowed), highest level of education (school/further education vs university), and UK region (England, Northern Ireland, and Wales).
The purpose of this study was to compare labor induction and cesarean delivery rates at term in community vs university hospitals.
30 Two studies compared clinicians (nurses vs surgeons; community vs university medical practices).
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To this end, we created dummy variables for hospital type (university vs non-university), ownership (public vs private) and for size (small, medium, large; based on the distribution of the number of beds in terciles).
All logistic regression models include the variables of age group, hospital type (university vs district) and deprivation (Townsend index quintiles 1 2 vs 3 5).
Data were also stratified by sex, type of hospital department (emergency room or ward, including outpatient clinic), length of stay (shorter vs 3 days or longer), type of diagnosis (primary or secondary), type of hospital (university vs regional), and calendar periods (1994 1999, 1999 2002, and 2003 2006).
In contrast, no differences in the PPV were found according to gender, different study periods or type of hospital (university vs regional) (Table 4).
We created dummy variables to introduce the categorical variables to the multiple regression models (university vs non-university hospitals; public vs private hospitals; and small vs medium vs large hospitals).
We will present detailed tabulations by patient (medical vs surgical), and hospital (university-affiliated vs community) characteristics.
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