Focus on the RRs and CIs (Relative Risk and Confidence Intervals)—for example: “cranberry products did not significantly reduce the occurrence of symptomatic UTI overall (RR 0.86, 95% CI 0.71 to 1.04)”.

A relative risk lower than 1.0 suggests a reduced risk, while the 95% confidence interval is the range that the real value probably falls within, accounting for potential sampling error, study size, etc.

So an RR of 0.86 suggests that cranberry probably reduces the risk of UTI, but the top of the CI being above 1.0 means the study can’t rule out the chance that cranberry actually increases the risk. It’s that last part that presumably leads them to say that cranberry didn’t reduce the risk significantly.

There's a couple thing to keep in mind that might help it make sense. Meta-analyses, though not without merit, don't always accomplish the intended goals for a few reasons, two of which this study specifically suffers from. The first is that they combine studies that when they were conducted may have looked at the desired treatments and controls, but they often are doing so in different populations (note they stratify results by elderly, cancer patients etc.) I'm not going to on my phone start pulling the references, but I would venture to guess that the included studies have all sorts of specific groups they were targeting which leads to heterogeniety both in effect size but in interpretation of effect size. Think about it this way, if you are using Dat from many studies with different (often very specific) target populations, does it really make sense to combine them to draw conclusions about a some hypothetical population comprised of those people?

The second thing is sample size. A few thousand seems like a lot until you realize the data in question is incidence. Each subject included either had the disease or they didn't (it's 0 or 1) nothing in-between and nothing outside. Interval inference for dichotomous data (especially when it gets substratified down like the authors have done here) often lead to results like the plain language summary presented. That is, everything is null because they tried to say too much, with too little data.

Takeaway is don't read too much into the findings. The authors were certainly trying to earnestly answer the question (probably), but the existing literature and available data came up short.

I’m too tired to read the article but everyone here is commenting on cranberry’s preventative performance. There’s a difference between prophylaxis and treatment - so that’s worth clarifying in my mind.

I've only skimmed the abstract, but it makes me think antibiotics aren't effective. I'm basing that on combining two findings that are explicitly stated there: cranberries don't work, and cranberries are no different to antibiotics. Transitive inference would imply that this means antibiotics don't work, although I'm surprised the authors haven't been more explicit about this, given they've left it ambiguous and it seems like an obvious question

Edit: there's slightly more detail at the bottom where it says "Cranberry products were not significantly different to antibiotics for preventing UTIs in three small studies." It looks like cranberries and antibiotics were only compared in a very limited set of studies, so perhaps take the comparison with a pinch of salt

No worries, I have to review a few meta -analysis a month. They are not all created equal. But some are trying to help.