If I have means and medians, is there a way to average them? Please consider these data points as an example of many more, they represent single studies with multiple samples which have been done during the last decades.
7.7 +/-3.3) mg/L, n=10
3.6 +/-0.2 mg/L, n=22

The publication states that 7.7 resp. 3.6 represents mean OR median, without specifying which has been used where, and there is no way to find out.
Is there a way to get something like an average out of those data points, or is too much crucial information missing?
EDIT:
This question should be deleted. I realized I misread the data in question: values with a standard deviation are means, values without are median. Thank you for your help, and sorry for bothering you.
 A: The answer depends on nature of your data. If you can assume that your data comes from an unimodal and symmetric distribution, then mean should be close to median, otherwise they could differ (see here for reading more on relations between mean and median). Below you can find a quick example:
x <- rexp(1000) # Exponential distribution (skewed)
mean(x)
## [1] 0.9910864
median(x)
## [1] 0.6553773

y <- rnorm(1000) # Normal distribution (symmetric)
mean(y)
## [1] -0.03853657
median(y)
## [1] -0.05458903



As I wrote in answer to your previous question, making some assumptions about distribution of your data would make it possible to make some educated guesses about your data (e.g. about mean given only a range, but it would be also possible to infer about mean given only a median using similar approach).
Also notice that if your sample sizes are small (n=10, n=22 as in your example), then you can expect the differences to be even bigger since a single outlier can possibly influence the mean.
