Methodological question: adjusting for confounders in logistic regression

I have three attributes in a dataset (D0), representing the binary response of success or failure (R), some form of treatment or treatment group (T), and a potential confounder (C) respectively. Building a logistic regression model (with eg. logit link) to predict R with T and C as covariates, I can get the log-odds of success for R, whilst adjusting for C.

My question is: does it make sense to generate an “adjusted dataset” D1, whereby R has been adjusted for C? To be more specific, T and C in D1 will be exactly the same as that of in D0, but the values of R in D1 will be different: R in D1 will be dictated by the coefficients in the logistic model.

I recognize that for simple OLS regression, this is trivial. But since logistic regression is predicting log-odds (and hence also predicting the probabilities Pr(R = success)), there is an additional step to convert the probabilities into actual binary values.

Typically, we can use a data-driven threshold to convert the probabilities into binary values, by looking at various metrics such as specificity, sensitivity, etc. But these are driven by predictions and accuracy – what I am looking at is statistical adjustments.

Any ideas? Is such a procedure of generating “adjusted datasets” with logistic regression even valid?

Thanks!