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Trip Database Blog

Liberating the literature

Top articles last year – Cardiology, Critical Care and Dentistry

Which articles were viewed most in 2016 for the following topics?

Cardiology

  1. Saline versus Heparin for Maintaining Patency of Central Venous Catheters: A Review of Clinical Effectiveness and Safety. CADTH
  2. Randomised controlled trial: Presence during cardiopulmonary resuscitation is beneficial to family members in the out-of-hospital setting. EBM
  3. Chronic heart failure – End-stage chronic heart failure. CKS
  4. Management of patients with stroke: rehabilitation, prevention and management of complications, and discharge planning. SIGN
  5. Guidelines on Prevention, Diagnosis and Treatment of Infective Endocarditis. European Society of Cardiology

Critical Care

  1. Guideline Summary: Surviving sepsis campaign: international guidelines for management of severe sepsis and septic shock: 2012., Society of Critical Care Medicine]
  2. Oral hygiene care for critically ill patients to prevent ventilator-associated pneumonia. Cochrane
  3. Intravenous immunoglobulin for treating sepsis, severe sepsis and septic shock. Cochrane
  4. An evaluation of the feasibility, cost and value of information of a multicentre randomised controlled trial of intravenous immunoglobulin for sepsis (severe sepsis and septic shock): incorporating a systematic review, meta-analysis and value of information analysis. NIHR HTA
  5. Guideline Summary: Traumatic brain injury medical treatment guidelines. Colorado Division of Workers’ Compensation

Dentistry

  1. Temporomandibular disorders (TMJ). CKS
  2. Dental interventions to prevent caries in children. SIGN
  3. Non-Fluoride Caries Preventive Agents. DARE
  4. Oral Health: Nursing Assessment and Intervention. Registered Nurses’ Association of Ontario
  5. Gingivitis and periodontitis. CKS

Emergency medicine, Endocrinology and Gastroenterology to follow.

New update currently being tested

Hopefully these changes will be out in very early 2017…

First up, the long-discussed Answer Engine:

new-trip-1-answer-engine

Second major update is the smart search (we follow your clicks to suggest closely related articles):

new-trip-2-smart-search

Next up is the better integration of the search suggestions:

new-trip-3-search-suggestions

Finally, two features here – an enhanced RobotReviewer assessment of bias display and a manual broken links system:

new-trip-4-misc

NOTE: ignore the numbers associated with each article, these will be removed after testing!

Do you work for the NHS in England?

If you work for the NHS England can you please complete this brief poll below.

This year Health Education England paid for a trial of Trip Pro for all staff in England and as we approach the end of the year we’re discussing what happens next year.  This poll should help inform these discussions. NOTE: To register your vote you need to press the ‘Vote’ button under each of the three questions.

If you have any other comments about access to Trip Pro, perhaps extra features to help support you in the NHS then please let us know via the comments or emailing me: jon.brassey@tripdatabase.com

Also, if you want Trip Pro to continue an email of support, sent to the above email would be very helpful!

 

Broken links, again

A search engine requires a number of key elements to be successful; one of which is the websites/links it sends people to are valid and useful!

The NIHR, a large UK-based research funder and publisher, moved to a new URL structure a while back. Initially, this wasn’t a problem as the old articles (and old URLs) still worked.  But that has recently changed.  We have over 1,000 articles in our index from the NIHR and around 50-60% suddenly became broken. So, we’ve rushed through a fix and everything is fine for now.  But while it was the NIHR this week it might be another publisher the week after.

It’s a constant battle.

In 2011 we introduced an automated system which initially worked very well but over time we’ve come to appreciate that it’s only a partial solution.  So, with our next upgrade (due early January) each article will have a new ‘broken links’ link:

trip-brokenlink-1-1

The ‘broken link’ link (underlined in red) will be activated by a user clicking on it. This will then cause our broken link system to be activated and hopefully swift action to correct it.

Trip, trying to improve every day.

The Answer Engine, first glimpse

We have got a number of developments that we want to get out before the end of the year, the most important being the answer engine.

The answer engine works by interpreting the search terms to infer the question and then using a variety of techniques to find the best answer.  This is then displayed at the top of the results.  An example is shown below:

answer-engine-acne-and-minocycline

In this example the user has searched for acne and minocycline and our system has interpreted this as being a question about the efficacy of minocycline in treating acne.  The system then looks for the best answer, in this case a Cochrane Systematic Review, and pulls through the conclusion.

The above is a mock-up and is likely to change somewhat, but it gives you an idea of what it looks like.

Initially, the system will be modest in scope and will be semi-automatic.  Our aim is to harness feedback and then make the appropriate changes.  By the middle of 2017 we hope to dramatically increase the scope and be fully automatic.

 

Dementia networks

We’ve been playing with our clickstream data – this time visualising it.  We’ve taken a single document Comorbidity and dementia: a mixed-method study on improving health care for people with dementia (CoDem) and mapped all the connected articles.  A connection is made if a user clicks on the above article and then, within the same search session, clicks on any other article(s).  We then use these connections to make some beautiful images, an example is below.  The article above is the big ‘blob’ towards the bottom of the image!

nihr-dementia

Search patterns in Trip

In preparation for the release of our answer engine (inferring clinical questions from the search terms and showing the ‘best’ answer) we’ve been analysing search terms. An area of particular interest are searches with a disease/condition and intervention (or similarly complex search). So, the top five search terms in Trip that follow this pattern are:

  • children paracetamol ibuprofen temperature
  • child cancer sibling parent
  • osteoarthritis glucosamine
  • pelvic floor strength
  • pressure ulcer prevention

I imagine few could have guessed that list!

But to illustrate the answer engine idea, if someone searches for osteoarthritis and glucosamine we’ll show – at the top of the results – this answer:

“Pooled results from studies using a non-Rotta preparation or adequate allocation concealment failed to show benefit in pain and WOMAC function while those studies evaluating the Rotta preparation showed that glucosamine was superior to placebo in the treatment of pain and functional impairment resulting from symptomatic OA.”

Which is taken from this Cochrane systematic review.

Marketing Trip

The Trip Database is just amazing. I love how it works and the features that it offers. But from my experience, it just doesn’t seem as though it is well-known or is getting the recognition from the scientific community that it deserves. What efforts are being done for marketing the Trip Database?

Sincerely,
Isaac M. E. Dodd
MD Student at Howard University College of Medicine

The above is not an uncommon type of email.  Users find Trip, love it and contemplate that it was perhaps accidental that they found it, that few of their colleagues know about it and that it should be more widely known.

One can rely on word of mouth, which works to an extent as we get hundreds of thousands of searches per month.  But to push on probably requires marketing!  Unfortunately, Trip’s marketing budget has historically been virtually zero.  I say virtually zero as I’m not sure if our various Twitter accounts count as marketing or not.

While marketing is not my strength I’m increasingly drawn to the need to do some!  The main aim being to raise awareness of Trip which will hopefully lead to more subscriptions. Historically, if we had money I’d put it towards product development not marketing.  But this is sort of self-defeating.  So, when confronted with something as vast as marketing – where does one start?

Do we:

  • Go down the social media route, embracing Twitter more (for instance)?
  • Try and use adverts?  Surely not as I doubt the engagement is there.
  • Work with 3rd parties in some mutually beneficial way? They get some product from Trip in return for raised profile of Trip.
  • Write more papers about the findings of Trip in peer-reviewed journals?

There we go, my marketing thoughts – completely unsophisticated – in one go.  I can think of variants of the above but nothing much more than that.

Clearly we need some help.  So, with a finite budget, what brings the best return on investment?

HELP!

 

Document summarisation

Complete stab in the dark, stimulated by Google’s release of their cutting edge TensorFlow product, is our adventure in to document summarisation.  The work below does not use TensorFlow, we’re starting gently with something a little easier to implement!  But the general idea is you take long documents and summarise them into something shorter and easier to digest.  All the work below involves automated methods and the summarisation is pretty much instant.

I’ve long held the idea (see Article social networks, meaning and redundancy) of trying to make sense of document clusters and this work is another exploration of this area.  So, I took 5 articles from the UTI and cranberry cluster mentioned in the article above, focusing on the prevention of UTIs and placed them through our test system.  Below are the results for 5 articles, with the title (with embedded URL to the actual abstract) and then the summary as generated by our system.

1) Cranberry juice fails to prevent recurrent urinary tract infection: results from a randomized placebo-controlled trial.
Summary: we conducted a double-blind, placebo-controlled trial of the effects of cranberry on risk of recurring uti among 319 college women presenting with an acute uti. conclusions.: among otherwise healthy college women with an acute uti, those drinking 8 oz of 27% cranberry juice twice daily did not experience a decrease in the 6-month incidence of a second uti, compared with those drinking a placebo.

2) Cranberry-Containing Products for Prevention of Urinary Tract Infections in Susceptible Populations: A Systematic Review and Meta-analysis of Randomized Controlled Trials
Summary: the aims of this study were to evaluate cranberry-containing products for the prevention of uti and to examine the factors influencing their effectiveness. medline, embase, and the cochrane central register of controlled trials were systemically searched from inception to november 2011 for randomized controlled trials that compared prevention of utis in users of cranberry-containing products vs placebo or nonplacebo controls.

3) A randomized clinical trial to evaluate the preventive effect of cranberry juice (UR65) for patients with recurrent urinary tract infection
Summary: the subjects drank 1 bottle (125 ml) of cranberry juice or the placebo beverage once daily, before going to sleep, for 24 weeks. in the group of females aged 50 years or more, there was a significant difference in the rate of relapse of uti between groups a and p (log-rank test; p = 0.0425).

4) Cranberries for preventing urinary tract infections
Summary: cranberry products significantly reduced the incidence of utis at 12 months (rr 0.65, 95% ci 0.46 to 0.90) compared with placebo/control. cranberry products were more effective reducing the incidence of utis in women with recurrent utis, than elderly men and women or people requiring catheterisation.

5) Recurrent urinary tract infection and urinary Escherichia coli in women ingesting cranberry juice daily: a randomized controlled trial.
Summary:  to compare the time to urinary tract infection (uti) and the rates of asymptomatic bacteriuria and urinary p-fimbriated escherichia coli during a 6-month period in women ingesting cranberry vs placebo juice daily. the proportion of women with p-fimbriated urinary e coli isolates during the intervention phase was 10 of 23 (43.5%) in the cranberry juice group and 8 of 10 (80.0%) in the placebo group (p=.07). minor adverse effects were reported by 24.2% of those in the cranberry juice group and 12.5% in the placebo group (p=.07).

Then, as a further twist I took the 5 summaries above and placed them through the summarisation software to give this ‘meta-summary’:

Among otherwise healthy college women with an acute uti, those drinking 8 oz of 27% cranberry juice twice daily did not experience a decrease in the 6-month incidence of a second uti, compared with those drinking a placebo. to compare the time to urinary tract infection (uti) and the rates of asymptomatic bacteriuria and urinary p-fimbriated escherichia coli during a 6-month period in women ingesting cranberry vs placebo juice daily.

Not sure it makes much sense!

So, as a further experiment I took all the conclusions from the 5 articles (as opposed to our summaries) and this is the result:

the potential protective effect we observed is consistent with previous studies and warrants confirmation in larger, well-powered studies of women with recurrent uti. there is some evidence that cranberry juice may decrease the number of symptomatic utis over a 12 month period, particularly for women with recurrent utis.

Perhaps it is just me, but this seems interesting.  Interesting as it’s not too bad and interesting because this could be fully automatic – we just need to automated the clustering and improve on the summarisation.  So, when we get Google’s cutting edge TensorFlow working (which uses deep learning) I’ll repeat the experiment and see where that takes us.

One small step along a winding and interesting journey!

 

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