This is very much a ‘thinking aloud’ post.
In October last year I posted Structure in Trip an article that described the social networks of articles in Trip, based on clickstream data. The analysis allowed me to produce graphs like the one below (based on the clickstream data of people searching for UTI.
The structure is clear and I’ve labelled a few, the most prominent being UTIs and cranberry (in the bottom left of the graph).
I’m increasingly of the opinion that this can be used to speed up the review process and also improve the search experience (but search is for another day). In social network analysis there is a view that within a cluster there is a lot of duplicated information. If you think about your social networks your close friends probably know lots of the same things as you – this duplicated information/knowledge about birthdays, addresses etc. I can’t help feeling this is likely in clusters of articles. So, take the cluster of UTI and cranberry there’s probably a lot of duplicated information (background information I would have thought). But there is also lots of unique information (e.g. each set of results will be unique). Then the conclusions are probably split into three main types – positive, negative, uncertain/ambiguous.
So, as a precursor to more in-depth work I simply took the articles and created a word-cloud (I did do some editing to remove terms I felt were unhelpful):
And this is the thinking aloud part – I’m not sure if that’s helpful or even useful. It’s pretty. But I really don’t think it’s hugely helpful as it stands. However, it does take me further along with my thinking, I think! Perhaps adding some specialist semantic analysis would be helpful.
Now, and this surely constitutes a world first – an instant systematic review. I’ve posted a few times about five minute systematic reviews, but if we could identify clusters, extract the RCTs and automatically put them into our rapid review system the result is an instant review. Well, I did just that (manually, but it could be automated to make it truly instant). There were 18 articles in the UTI and cranberry cluster. Many of these were review articles but there were also 4 placebo-controlled trials. Placing them in our rapid review system gives the following result:
So, our system gives it a score of -0.12, so I would say that the results show no clear evidence for or against the effectiveness of cranberry juice in UTIs. One point, one of the trials is for a highly-specific population ‘patients undergoing radiotherapy for cancer of the bladder or cervix’ so ideally would be excluded.
How does our score of -0.12 compare with the Cochrane Cranberries for preventing urinary tract infections? Within their conclusion, they report:
“..cranberry juice cannot currently be recommended for the prevention of UTIs“
Lots of things to reflect on. I would say our conclusion is pretty much the same as Cochrane’s. Ours is based on far fewer trials. This, in part, reflects the fact that our cluster was only based on a small sample of all our data, so a full analysis would highlight more trials. But I think the principle is there, instant systematic reviews – easy!
I’m hoping I’ll look back at this post in 2-3 years time and laugh at how basic the analysis is – reflecting a significant leap forward in my work.