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Our objectives were to develop a universal surgical risk estimation tool, to compare performance of the universal vs previous procedure-specific surgical risk calculators, and to allow surgeons to empirically adjust the estimates of risk.
The third step of the analysis was to adjust the estimates by including the following demographic and clinical variables collected at baseline: sex, age, body mass index, presence of diabetes, systolic and diastolic blood pressure, HDL cholesterol, total cholesterol, c-reactive protein, current smoking status, and waist-to-hip ratio.
We took measures to adjust the estimates for the differences at baseline.
For this reason we did not adjust the estimates presented for sociodemographic or other covariates.
Random effects for patients will be included to adjust the estimates for intraindividual correlations.
In these cases, the profile of pregnant women will be used to adjust the estimates of these parameters.
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Methods which attempt to adjust the estimated treatment effect, either through adjustment to the hazard ratio or via accelerated failure time models, were also considered.
It is important to adjust the estimated missing values to the overall size of the compositional parts of the neighbors.
The model uses commonly available cost indices (CCI and BCI) to adjust the estimated costs for the time and location of the project.
We fitted exponential functions to the empirical observations, and in case of the real data set we used the value of these functions to adjust the estimated probabilities in order to adjust the estimated precision.
Therefore, we adjust the estimated kwaDukuza PHC clinic budget by adding the annual spending on the ART program (Table 4).
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CEO of Professional Science Editing for Scientists @ prosciediting.com