How can one summarize categorical variables with frequency variables in a clinical study? Quote:

All eligible patients were analyzed. For the background of the patient population, categorical variables were summarized with the frequency and rate and continuous variables with fundamental statistics. The PFS and OS were estimated using the Kaplan-Meier method. Similarly, the PFS was estimated in subgroups.

I don't get this. How can one summarize incompatible types of data in order to understand the background (medical history) of patients? 
And what is the meaning of "with fundamental statistics"? Some "fundamental statistical data" were summarized with the preceding variables, or some "fundamental statistical methods" were used to summarize the preceding variables? 
Basically, I can't understand the meaning of the whole sentence. What did they do, in simple words?
Maybe the word summarize here does not imply "summation" but just stands for "we investigated this, we made a short summary of this"?
If the word "summarize" does mean "summation", then what was actually summarized with what? Is there any way to exlpain it in simple terms? It reads like gibberish to me.
(Cross-posted on Medical Sciences SE)
 A: Say you are a doctor looking at the results of this study. To understand if these results are applicable to patients in your practice, you need to understand first what kind of patients were included in the study. 
So you are going to look at information on the gender, age, etc., of the study patients to see to what extent that information mirrors the corresponding information for patients in your own practice.
Gender is a categorical variable - say it had the categories male, female and intersex in the study. So you are going to look at the frequency distribution of this variable for the study patients. In other words: how many patients in the study were male, how many patients were female and how many patients were intersex? Let's say the study included 100 patients in total, of which 50 were male, 40 were female and 10 were intersex. You could also look at the percentage of male, female and intersex patients in the sample (e.g., 50% of the patients were male, 40% were female and 10% were intersex).
Age is a continuous variable so "fundamental statistics" for age would refer to the "typical" age for the study patient and some measure of variation around that. For example, the median age of the 100 study patients was 45 years and 50% of these patients had ages ranging from 35 to 55 years. 
As you can see, it is entirely possible to describe each variable separately from the others. The nature of the description will depend on the type of variable. In my answer, I illustrated possible descriptions for categorical and continuois variables, but there are other possible variables out there (e.g., count variables). 
As @peteR pointed out, you can summarize a continuous (or a count) variable separately for each level of a categorical variable. For example, you can report the typical age for male patients, the typical age for female patients and the typical age for intersex patients in the study. That information would be quite helpful. 
If you need to refresh your statistics knowledge, I highly recommend that you do it, especially if you need that knowledge to perform your work. With statistics, taking shortcuts can be dangerous - you really need to understand what you are doing and why. Other people can't always give you shortcuts - it is up to you to invest the appropriate time into your own learning. 
