Hi, just to add to the comments above, bear in mind that randomization, whether it is simple, stratified or covariate adaptive, even if done properly, by itself, does not guarantee absolute balance on all relevant baseline variables. It only ensures that any imbalance that may occur, does so by chance, and not due to patient selection bias. This is why performing formal inter-arm, null hypothesis test based comparisons of baseline characteristics (e.g. so-called table 1) is pointless.
That being said, if there is not a typo in your post and there was indeed 0.9 (90%) males in one arm, versus 0.12 (12%) males in the other, it would be worthwhile to review your randomization process to be sure that it was implemented properly. That is a significant imbalance, and if your study had a DSMB monitoring it, this should have been noted much earlier in the study timeline for review and any correction in the randomization process that may have been apropos at the time.
Depending upon how your randomization schedule was created, vis-a-vis block sizes and related considerations, partial block use can also contribute to an imbalance. This is why block size, and whether that is fixed (e.g. all 4), or permuted (e.g. 2 and 4 in random sequences), is an important consideration in implementing the randomization schedule.
There is a general phrase that is apropos which is, “Analyze as you randomize”, and which I believe is attributed to Fisher, though somebody will correct me. As Frank notes, this should all be pre-defined in an SAP for the study.
Thus, there is no motivation to leave gender out of your multivariable model, indeed, as Pavlos indicated, it increases the need to keep it in.