Difference between generalized logistic regression and logistic regression I have received a weird comment from a referee of pretty decent Journal. I stated in the methods section that "The association of the exposure with the outcome was investigated in terms of odds ratio (OR; 95% confidence interval, CI) using generalized logistic regression".  Both outcome and exposure were binary.
Basically, this is what I did with R:
fit <- glm(outcome ~ exposure,  family = 
           binomial(link = "logit"), 
           data = df)

The referee commented this: “I would like to know why the authors used generalized logistic regression as opposed to conventional logistic regression. What is the extension part of the model (generalisation) doing? What was it used for? Were you trying to adjust for the clustering of participants within recruitment sites? You will need a multilevel model (not a GLM) for that."
What do you think is the true difference between generalized and conventional in this setting (also in code writing)?

There's generalised linear modelling GLM (a tool which is general in that it accomodates non-linear functions, in your case: logistic) and there's generalised logistic function (which is general in that it extends the "classical" logistic function). Mentioning the latter while meaning the former might have left the reviewer wondering about the priorities (elaborating on the logistic function while - from all the rev knew - the clustering of participants would've been prior concern, if any, to be addressed with a multi-level rather than GLM tool).
(sorry, I know this should go into a comment, for which I'd need 50 rep though)
