Very wide confidence intervals for odds ratios I have estimated a binary logit and calculated the odds ratios and their LR confidence intervals as follows:

As you see some of these intervals are extremely wide. I guess it is due to insufficient variability in those variables (but I am not sure). How should I interpret these in a research paper? Should I just say (e.g. for C8) "c unit increase in x changes the odds of success by 1.9086 to 476.4251 times, with 95% confidence"? I am sure the reviewers will find this and point at it. What is your suggestion?
 A: I am not familiar with the standards in this area, but note that (as suggested by @jjet in the comments) the numerical value of an odds will vary strongly with the value of the underlying probability. So "linear intuitions" may be misleading.
If the odds is defined by
$$o=\frac{p}{1-p}$$
then its sensitivity will be
$$\frac{do}{dp}=\big(1+o\big)^2$$
Another way to look at it is that the change in $o$ will be exponential relative to a change in the $\beta$ coefficient that multiplies $x$.
I myself would probably report probabilities rather than odds (as indicated by @jjet). Or alternatively you could just report log-odds. However I would check similar articles in your field to see what the standard is for reporting.
(Note: This is all assuming you are confident there are not artifacts in the results, as suggested in Dave Harris's answer.)
A: There is a very good paper on this at http://www.scielosp.org/pdf/rpsp/v2n4/v2n4a7.pdf
There are multiple reasons for this, including software failure.  They test eight standard software tools and set in motion a configuration of data that should trigger software failure.
As to causes, they list 

These problems include, inter alia, the instability of the model due to
  inadequate sample size; the problem of “complete separation” that occurs
  when all subjects whose outcome variable is equal to 1 can be perfectly separated from those whose outcome variable is equal to 0, based on their
  characteristics; the colinearity problem; and, finally, the problem of profiles
  with a frequency equal to 0.

Rather than opine on possible causes and as you know your data set, I suggest reading the paper and taking a close look at your data.  They run eight programs on the same data set and get eight very different answers for coefficients.
