Is "unadjusted" basically just simple linear regression whereas "adjusted" is multiple regression? For example, looking at the effect of x on y adjusting for other variables like a, b and c versus not adjusting for them.
Since based on the comments "Yes" isn't long enough to be an answer:
When a regression reports an unadjusted estimate, it's just a regression of X on Y with no other covariates. An adjusted estimate is the same regression of X on Y in the presence of at least one covariate.
Crude estimate is obtained when you are considering the effect of only one independent (predictor) variable, i.e your equation consists of only one independent variable. When you include more independent variables in the analysis (confounder variables) you will get what is called and adjusted estimate, which takes into account the effect due to all the additional independent variables included in the analysis.
All above is true, I just want to add that the adjusted is when you consider multiple covariants or independent variables (for example: X1, X2, X3, X4), set them all constant at their 'Mean Value' except one Independent variable (X1) to capture the relationship between this one independent variable and the dependent variable (X1 and Y).