I Need Someone to Translate Statisticianese

I'm researching for an article I'm writing and seeking an answer to whether pharmaceutical advertising leads to more pill distribution. From the amount of money they spend on it, it's pretty obvious that pharmaceutical firms believe it does, but I was was looking for empirical data (or at least someone explaining the empirical data in terms I can understand).  I found an article in an Indian news site and from Reuters talking about a new study out that reviewed a bunch of other studies and indicated that doctors "prescribe more expensively, less appropriately and more often" because of pharmaceutical advertising.  However, I was a little suspicious because no US news sites seemed to have picked this up and I prefer original sources anyway, so I looked up the study.

As best I can tell the article's conclusion is that it is a definite possibility that pharmaceutical advertising might perhaps have caused increased cost and decreased quality in prescriptions (maybe). There is "some evidence of increased costs and decreased quality of prescribing." So, I dug into the article itself, trying to find the basis of the two news reports and I think that this portion may be what they focused on (primarily I base this on Reuters talking about the same numbers as are found in this paragraph, albeit probably terribly misconstruing them):

Of the 58 studies included in this review, 38 studies reported a single unit of analysis with 25 (66%) finding significant associations between exposure to information from pharmaceutical companies and the quality, frequency, and cost of prescribing and eight (21%) finding no associations. The remaining five (13%) had multiple measures and found significant associations on some measures but not on others. The 20 studies with more than one unit of analysis reported 49 units of analysis of which 21 (43%) found significant associations, 24 (49%) found no associations, and four (8%) found mixed results. The difference between the results of the single versus multiple unit of analysis studies is significant (p<0.05 Freeman-Halton extension of the Fisher exact test). This difference may have been caused by publication bias against publication of single unit of analysis studies when no association was found. We believe the pattern of results suggests that there was little or no reporting bias for the multiple unit of analysis studies. Because the multiple unit of analysis studies found no association more often than the single unit of analysis studies, multiple mentions of the former studies in our narrative synthesis will not exaggerate the frequency of findings of significant associations.

Okay, I need some translation for the following terms: "single unit of analysis", "multiple measures", and "multiple units of analysis." I have in my head what I think those mean, but shan't give my thoughts here because I'd rather have someone tell me fresh, rather than tip-toeing around any misunderstanding I might have.

As well, am I right in understanding that the authors applied their own perceptual, unsupported bias to deprecate the "single unit of analysis" results?