Search

Trip Database Blog

Liberating the literature

Connected/related articles

This post is an attempt to ‘think aloud’ about connected/related articles…. By that I mean, if you find an article you like how can you quickly find others that are similar. We know that searching is imprecise and a user might find articles that match their intention at say result #2, #7, #12 and then they may lose interest and miss ones at #54 or #97.

At Trip we have had something called SmartSearch for years. This mines the Trip weblogs to highlight articles that have been co-clicked in the same search session. So, if a user clicks on articles #2, #7 and #12 we infer a connection. We have successfully mapped these connections and it reveals a structure in the data. In the example below it’s a small sample of connections taken from searches for urinary tract infections:

Each blue square represents a document and the lines/edges are connections made by co-clicking the documents within the same search session. You can see from the annotation that these form clusters around topics. However, co-clicking is not perfect!

Fortunately, there are other types of connections that I think we can use – semantic similarity and citations.

Semantic similarity: I’m thinking principally of PubMed’s related articles. This uses statistical methods to find articles with similar textual information e.g.

At the top is the document of interest and below are the articles deemed semantically similar. So, these articles are all related and one could make connections between them.

Citations: Articles typically list a bunch of references – so the article is citing these. And any article can itself be cited. So, you have forward and backward citations. Again, these have been shown as connections and mapped, e.g (source):

So, three types of connections: co-click, semantic similarity and citations. In isolation all have their issues but combined it could be something incredibly powerful. Well, that’s the theory….

While I believe SmartSearch is brilliant, I don’t think we’ve implemented it particularly well. The main issue I have is that a user needs to ‘call’ the results. On one hand that’s not a big deal but it looks like this:

I’ve highlighted it in red so you don’t miss it (an important issue in itself) but also it’s not really telling the user why they should click. In other words, it has a weak ‘call to action’. In part this is because it’s not ‘real time’ – a user clicks a button and the system calculates the related articles. I’m thinking if we told users that there were, say, 25 closely connected articles and 7 very closely connected articles, possibly teasing what these were, it would be much more compelling.

Another consideration, the notion of connected articles can work on two levels: the individual article and a collection of articles.

Individual articles: Each article within Trip could feature other connected articles be it co-clicks, semantic similarity or citations. It could be that we create a badge (thinking of the Altmetric Donut) that helps indicate to users how many connections there might be.

Collection of articles: If a user clicks on more than one article we, in effect, add up the information from the individual article data. This allows for some clever weightings to be brought in to highlight particularly important/closely connected articles.

But what information is important/useful to the user? I’m seeing two types of display:

List: A list of articles, arranged by some weighting to reflect ‘closeness’ – so those at the top are deemed closer to the article(s) chosen. We could enhance that by indication which are systematic reviews, guidelines etc

Chronological list: As above but arranged by date. The article(s) chosen would be shown and then a user could easily see more recent connected papers and also more historical papers. The former being particularly useful for updating reviews!

Right, those are my thoughts, for now. They seem doable and coherent but am I missing something? Could this approach be made more useful? If you have any thoughts please let me know either in the comments or via email: jon.brassey@tripdatabase.com

Assessing systematic review quality (automatically)

We’re keen to help users use the best quality evidence to inform their decisions. While we use the pyramid to help express the hierarchy of evidence there is a danger of that being too simplistic. For instance, not all systematic reviews are high-quality and some are, frankly, terrible.

We have been working on quality scores for RCTs and guidelines for some time and these should both be released by early 2023. However, of equal importance, is scoring systematic reviews. Given Trip covers hundreds of thousands of systematic reviews, any tool we introduce needs to be automated. Well, we’ve taken the first tentative steps…

We have devised a scoring system, capable of automation, and trialled this on a sample of 32 systematic reviews. We knew the assessments of the 32 prior to starting and the scoring was done by a 3rd party and was freely available data on the web (using ROBIS the scores were low, high or unclear risk of bias). We then correlated our scores against the ROBIS scores and this is what the graph looks like:

The Y-axis is our score (range from -3 to 8) and the x-axis is simply the number of the systematic review (so 15 were graded as low risk of bias, 9 as high risk of bias and 8 as unclear).

For a first attempt the results are impressive and shows the validity of the approach. The average score per risk of bias category is as follows:

  • Low – 5.3
  • Unclear – 3.75
  • High – 0.78

We clearly need to spend more time on this trying to understand why, for instance, the 3rd ‘low risk of bias’ systematic review scored so low in our system. But there’s time for that, time to adjust weighting, possibly add or remove scoring elements.

Bottom line: we’re well on the way to rolling out an automated systematic review scoring system that can help Trip users make better use of the evidence we cover

Systematic reviews in Trip – a quick update

After our recent post on the subject I thought I’d explore the new systematic reviews added to Trip. So, for the last week we uploaded 829 new systematic reviews from PubMed. To give a flavour of the coverage, here are the sample of the most recent:

  • The role of noninvasive scoring systems for predicting cardiovascular disease risk in patients with nonalcoholic fatty liver disease: a systematic review and meta-analysis.
  • A systematic review on microplastic pollution in water, sediments, and organisms from 50 coastal lagoons across the globe.
  • The effects of exposure to environmentally relevant PFAS concentrations for aquatic organisms at different consumer trophic levels: Systematic review and meta-analyses.
  • Provisional Versus Dual Stenting of Left Main Coronary Artery Bifurcation Lesions (from a Comprehensive Meta-Analysis).
  • The Impact of Cognitive Impairment on Clinical Outcomes After Transcatheter Aortic Valve Implantation (from a Systematic Review and Meta-Analysis).
  • A meta-analysis of the genetic contribution estimates to major indicators for ketosis in dairy cows.
  • Heterojunction photocatalysts for the removal of nitrophenol: A systematic review.
  • The effect of rhythmic movement on physical and cognitive functions among cognitively healthy older adults: A systematic review and meta-analysis.
  • Effectiveness of multicomponent training on physical performance in older adults: A systematic review and meta-analysis.
  • Molecular mechanism of the anti-inflammatory effects of plant essential oils: A systematic review.

An interesting mix, that’s for sure, and we should possibly explore removing non-human studies!

The above is a sample from PubMed, we also get systematic reviews from other sources:

  • Grey literature, which we explore on a manual and monthly basis – this includes a host of Health Technology Assessments
  • Third-party sources

The latter is not yet automated, but will be shortly. So, it wouldn’t surprise me if we don’t add 1,000+ systematic reviews to Trip every week!

There’s an awful lot of systematic reviews being carried out!

Systematic reviews in Trip

The move to a new, stable system, has allowed us to start really improving the quality of Trip. Trip is a hugely valuable tool, but it isn’t perfect and the old system was creaking.

One immediate area for attention has been the way we grab systematic reviews. We have three main ways of adding systematic reviews to Trip:

  • A number of publishers are considered producers of systematic reviews and their content is not routinely added to PubMed – so we manually grab those records.
  • PubMed – we use a filter to identify systematic reviews
  • Others – we try to identify systematic reviews from a small number of third-party sources

The middle one, PubMed filter, is a complex area to navigate given the tension between sensitivity and specificity. Too sensitive (to identify ALL systematic reviews) and you bring in a load of false positives. Too specific (to only identify TRUE systematic reviews) and you miss a load of systematic reviews – false negatives.

So, we’ve been carrying out a lot of tests on PubMed and have plumped for this filter:

(systematic review[sb] OR meta analy*[TI] OR metaanaly*[TI] OR “Meta-Analysis”[PT] OR “Systematic Review”[PT] OR “Systematic Reviews as Topic”[MeSH] OR “systematic review” [TI] OR “health technology assessment” [TI] OR “Technology Assessment, Biomedical”[Mesh])

At the time of writing the above search identifies 372,212 results (click here to try it yourself). We estimate the other sources contribute an additional 80-100,000 systematic reviews. So, we’re on our way to half a million!

The new PubMed filter will also be checked much more regularly than previously and the third option (third-party sources) are next – again improved filter and more regular checking.

Systematic reviews are hugely important in the EBM world and therefore we’re delighted with progress and we hope our users will be too.

 

An error with the systematic review category

Our new index was seemingly all fine, but, we now know that we have a problem!! The problem lies with how we categorise articles as systematic reviews. So, you will find that many articles are being incorrectly labelled as systematic reviews. Apologies for this and we’re racing to fix this.

New system new journals

The release of the new update to Trip (A momentous milestone) has, so far, gone without a hitch. And, as such we’re building on this and improving the way we work. One area has been how we grab articles from PubMed. We’ve found some important issues that affected the timeliness of adding documents and these are being ironed out. We have also taken the opportunity to review the journals we add to Trip.

Years ago, when we started adding journals to Trip, we started with around 25 ‘core’ journals. That then expanded to around 100 and more recently (possibly last 7-8 years) we increased it to around 450. We typically focussed on journals with a high impact factor and ones that were clinically focussed. We had no desire – and still don’t – to include all 5000+ journals that PubMed currently includes.

With our review we have identified a number of new journals to add. These might have been new publications that weren’t available when we last reviewed journals and other might have risen up the impact factor ‘ranks. In total we’ll be adding just over 100 extra journals and these include the likes of:

  • Nature Medicine
  • Lancet Global Health
  • Annual Review of Public Health
  • Lancet Digital Health
  • Journal of Clinical Investigation
  • JAMA network open
  • Journal of Thrombosis and Haemostasis
  • Sports medicine
  • Military Medical Research
  • Health systems in transition
  • Social Science and Medicine
  • BMJ Quality and Safety

With the solid base of a new system improving Trip is becoming so much easier. The next year should see dramatic improvements across the site.

A momentous milestone

This weekend Trip released the latest update; hopefully you won’t have noticed. The new index has gone live! Last year we released the new site design, that was a rewrite of the front-end of the site, the bit users interact with. The latest rewrite, which went live yesterday, was the back-end. The index is the bit that’s responsible for how we grab and process new documents. Using a car analogy – the front-end is the bodywork and the back-end is the engine!

We announced the rewrite back in February 2020 and I said at the time it will be a massive undertaking, but it was even more complex than we imagined. But, it was necessary. Some nerd bits here:

  • We’ve moved from a monolithic architecture to a series of cloud-based microservices.
  • We’re using the latest javascript framework for both front and back ends.
  • Out has gone Cold Fusion and C# and in has come React and nodeJs.
  • In total there are now over half a million lines of code.

Due to the rewrite we have not really been developing new features to the site, but we’ve been planning them. The next few weeks will be monitoring the site and checking things are working as expected. After that, we can start to move forward with improving the functionality of the site.

One important thing to note, the results will be slightly different from the results you’d have got last week and this is for two reasons:

  • Fewer results – due to us removing a load of dead links that had accumulated in the index.
  • More results – due to us improving the automated grabbing of articles from the likes of clinicaltrials.gov, RCTs from RobotReviewer.

If you spot any problems then please let me know: jon.brassey@tripdatabase.com

Patient information

Trip has had patient information for many years. The logic being that a health professional may see a patient and then want to print off a patient information leaflet (PIL) to hand to the patient. Recently a contact asking if we’d heard of PIF TICK, we hadn’t! PIF TICK is:

Having the PIF TICK on leaflets, websites, videos or apps shows an organisation’s health information has been through a professional and robust production process. To be awarded the PIF TICK an organisation must show its health information production process meets 10 criteria.

To cut a long story short, we had a great conversation with Patient Information Forum (the ‘PIF’ in ‘PIF Tick’) and they alerted the organisations with the PIF TICK that Trip was interested in adding their content. Since then we’ve had lots of great new content from the likes of Target Ovarian Cancer, Crohn’s & Colitis UK and Mesothelioma UK.

This is a great initiative so please let us know of any great sources of patient information. Although PIF TICK is a UK-based initiative, we want global content. Don’t be shy…

(b)locked by Twitter

I’ve just received the following from Twitter:

The article in question was Efficacy of single-dose and double-dose ivermectin early treatment in preventing progression to hospitalization in mild COVID-19: A multi-arm, parallel-group randomized, double-blind, placebo-controlled trial. This is featured in Trip as ‘Key Primary Research’ as it has gone through additional layers of quality control via the wonderful EvidenceAlerts system. As they say on their site:

EvidenceAlerts is an Internet service that notifies physicians and researchers about newly-published clinical studies. Researchers at the McMaster Health Information Unit find the highest quality studies, reviews, and evidence-based clinical practice guidelines from 112 premier clinical journals and these articles are rated by practicing physicians for clinical relevance and interest. Alerts are curated to your own clinical interests.

Their rating for that particular paper (the banned one) can be seen here. The raters gave it this score:

The conclusion of the actual paper was:

Conclusion: Single-dose and double-dose ivermectin early treatment were not superior to the placebo in preventing progression to hospitalization and improving clinical course in mild COVID-19.

So, is the mis-information the claim that ivermectin is no better than placebo?

There is so much information out there on ivermectin e.g.:

We found no evidence to support the use of ivermectin for treating COVID-19 or preventing SARS-CoV-2 infection” (Cochrane)

Why You Should Not Use Ivermectin to Treat or Prevent COVID-19” (FDA)

The current evidence on the use of ivermectin to treat COVID-19 patients is inconclusive. Until more data is available, WHO recommends that the drug only be used within clinical trials” (WHO)

Do not use ivermectin to treat COVID-19 except as part of an ongoing clinical trial” (NICE)

This is appalling!

UPDATE: the ban was temporary and our account is now back!

Blog at WordPress.com.

Up ↑