Problems with Disappearing Set Numbers in PubMed’s Clinical Queries

18 10 2010

In some upcoming posts I will address various problems related to the changing interfaces of bibliographic databases.

We, librarians and end users, are overwhelmed by a flood of so-called upgrades, which often fail to bring the improvements that were promised….. or which go hand-in-hand with temporary glitches.

Christina of Christina’s Lis Rant even made rundown of the new interfaces of last summer. Although she didn’t include OVID MEDLINE/EMBASE, the Cochrane Library and Reference manager in her list, the total number of changed interfaces reached 22 !

As a matter of fact, the Cochrane Library was suffering some outages yesterday, to repair some bugs. So I will postpone my coverage of the Cochrane bugs a little.

And OVID send out a notice last week: This week Ovid will be deploying a software release of the OvidSPplatform that will add new functionality and address improvements to some existing functionality.”

In this post I will confine myself to the PubMed Clinical Queries. According to Christina PubMed changes “were a bit ago”, but PubMed continuously tweaks  its interface, often without paying much attention to its effects.

Back in July, I already covered that the redesign of the PubMed Clinical Queries was no improvement for people who wanted to do more than a quick and dirty search.

It was no longer possible to enter a set number in the Clinical Queries search bar. Thus it wasn’t possible to set up a search in PubMed first and to then enter the final set number in the Clinical Queries. This bug was repaired promptly.

From then on, the set number could be entered again in the clinical queries.

However, one bug was replaced by another: next, search numbers were disappearing from the search history.

I will use the example I used before: I want to know if spironolactone reduces hirsutism in women with PCOS, and if it works better than cyproterone acetate.

Since little is published about this topic,  I only search for  hirsutism and spironolactone. These terms  map correctly with  MeSH terms. In the MeSH database I also see (under “see also”) that spironolactone belongs to the aldosterone antagonists, so I broaden spironolactone (#2) with “Aldosterone antagonists”[pharmacological Action] using “OR” (set #7). My last set (#8) consists of #1 (hirsutism) AND #7 (#2 OR #6)

Next I go to the Clinical Queries in the Advanced Search and enter #8. (now possible again).

I change the Therapy Filter from “broad”  to “narrow”, because the broad filter gives too much noise.

In the clinical queries you see only the first five results.

Apparently even the clinical queries are now designed to just take a quick look at the most recent results, but of course, that is NOT what we are trying to achieve when we search for (the best) evidence.

To see all results for the narrow therapy filter I have to go back to the Clinical Queries again and click on see all (27) [5]

A bit of a long way about. But it gets longer…

The 27 hits, that result from combining the Narrow therapy filter with my search #8 appears. This is set #9.
Note it is a lower number than set #11 (search + systematic review filter).

Meanwhile set #9 has disappeared from my history.

This is a nuisance if I want to use this set further or if I want to give an overview of my search, i.e. for a presentation.

There are several tricks by which this flaw can be overcome. But they are all cumbersome.

1. Just add set number (#11 in this case, which is the last search (#8) + 3 more) to the search history (you have to remember the search set number though).

This is the set number remembered by the system. As you see in the history, you “miss” certain sets. #3 to #5 are for instance are searches you performed in the MeSH-database, which show up in the History of the MeSH database, but not in PubMed’s history.

The Clinical query set number is still there, but it doesn’t show either. Apparently the 3 clinical query-subsets yield a separate set number, whether the search is truly performed or not. In this case  #11 for (#8) AND systematic[sb], #9 for (#8) AND (Therapy/Narrow[filter]). And #10 for (#8) AND the medical genetics filter.

In this way you have all results in your history. It isn’t immediately clear, however, what these sets represent.

2. Use the commands rather than going to the clinical queries.

Thus type in the search bar: #8 AND systematic[sb]

And then: #8 AND (Therapy/Narrow[filter])

It is easiest to keep all filters in Word/Notepad and copy/paste each time you need the filter

3. Add clinical queries as filters to your personal NCBI account so that the filters show up each time you do a search in PubMed. This post describes how to do it.

Anyway these remain just tricks to try to make something right that is wrong.

Furthermore it makes it more difficult to explain the usefulness of the clinical queries to doctors and medical students. Explaining option 3 takes too long in a short course, option 1 seems illogical and 2 is hard to remember.

Thus we want to keep the set numbers in the history, at least.

A while ago Dieuwke Brand notified the NLM of this problem.

Only recently she received an answer saying that:

we are aware of the continuing problem.  The problem remains on our programmers’ list of items to investigate.  Unfortunately, because this problem appears to be limited to very few users, it has been listed as a low priority.

Only after a second Dutch medical librarian confirmed the problem to the NLM, saying it not only affects one or two librarians, but all the students we teach (~1000-2000 students/university/yearly), they realized that it was a more widespread problem than Dieuwke Brand’s personal problem. Now the problem has a higher priority.

Where is the time that a problem was taken for what it was? As another librarian sighed: Apparently something is only a problem if many people complain about it.

Now I know this (I regarded Dieuwke as a delegate of all Dutch Clinical Librarians), I realize that I have to “complain” myself, each time I and/or my colleagues encounter a problem.

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When more is less: Truncation, Stemming and Pluralization in the Cochrane Library

5 01 2010

I’m on two mail lists of the Cochrane Collaboration, one is the TSC -list (TSC=Trials Search Coordinator) and the other the IRMG-list. IMRG stands for Information Retrieval Methods Group (of the Cochrane). Sometimes, difficult search problems are posted on the list. It is challenging to try to find the solutions. I can’t remember that a solution was not found.

A while ago a member of the list was puzzled why he got the following retrieval result from the Cochrane Library:

ID Search Hits
#1 (breast near tumour* ) ….. 254
#2 (breast near tumour) …… 640
#3 (breast near tumor*) ….. 428
#4 (breast near tumor) …… 640

where near = adjacent (thus breast should be just before tumour) and the asterisk * is the truncation symbol.  At the end of the word an asterisk is used for all terms that begin with that basic word root. Thus tumour* should find: tumours and tumour and thus broaden the search.

The results are odd, because #2 (without truncation) gives more hits than #1 (with truncation), and the same is true for #4 versus #3. One would expect truncation to give more results. What could be the reason behind it?

I suspected the problem had to do with the truncation. I searched for breast and tumour with or without truncation (#1 to #4) and only tumour* gave odd results: tumour* gave much less results than tumour. (to exclude that it had to do with the fields being searched I only searched the fields ti (title), ab (abstract) and kw (keywords))

Records found with tumour, not with tumour*, contained the word tumor (not shown). Thus tumour automatically searches for tumor (and vice versa). This process is called stemming.

According to the Help-function of the Cochrane Library:

Stemming: The stemming feature within the search allows words with small spelling variants to be matched. The term tumor will also match tumour.

In addition, as I realized later, the Cochrane has pluralization and singularization features.

Pluralization and singularization matches Pluralized forms of words also match singular versions, and vice versa. The term drugs will find both drug and drugs. To match either just the singular or plural form of a terms, use an exact match search and include the word in quotation marks.

Indeed (tumor* OR tumour*) (or shortly tumo*r*) retrieves a little more than tumor OR tumour: words like tumoral, tumorous, tumorectomy. Not particularly useful, although it might not be disadvantagous when used adjacent to breast, as this will filter most noise.

tumor spelling variants searched in the title (ti) only: it doesn't matter how you spell tumor (#8, #9, #10,#11), as long as you don't truncate (while using a single variant)

Thus stemming, pluralization and singularization only work without truncation. In case of truncation you should add the spelling variants yourselves if case stemming/pluralization takes place. This is useful if you’re interested in other word variants that are not automatically accounted for.

Put it another way: knowing that stemming and pluralization takes place you can simply search for the single or plural form, American or English spelling. So breast near tumor (or simply breast tumor) would have been o.k. This is the reason why these features were introduced in the first way. 😉

By the way, truncation and stemming (but not pluralization) are also features in PubMed. And this can give similar and other problems. But this will be dealt with in another blogpost.

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Adding Methodological Filters to MyNCBI

26 11 2009

Idea: Arnold Leenders
Text: “Laika”

Methodological Search Filters can help to narrow down a search by enriching for studies with a certain study design or methodology. PubMed has build-in methodological filters, the so called Clinical Queries for domains (like therapy and diagnosis) and for evidence based papers (like theSystematic Review subset” in Pubmed). These searches are often useful to quickly find evidence on a topic or to perform a CAT (Critical Appraised Topic). More exhaustive searches require broader  filters not incorporated in PubMed. (See Search Filters. 1. An Introduction.).

The Redesign of PubMed has made it more difficult to apply Clinical Queries after a search has been optimized. You can still go directly to the clinical queries (on the front page) and fill in some terms, but we rather advise to build the strategy first, check the terms and combine your search with filters afterwards.

Suppose you would like to find out whether spironolactone effectively reduces hirsutism in a female with PCOS (see 10+ 1 Pubmed Tips for Residents and their Instructors, Tip 9). You first check that the main concepts hirsutism and spironactone are o.k. (i.e. they map automatically with the correct MeSH). Applying the clinical queries at this stage would require you to scroll down the page each time you use them.

Instead you can use filters in My NCBI for that purpose. My NCBI is your (free) personal space for saving searches, results, PubMed preferences, for creating automatic email alerts and for creating Search Filters.
The My NCBI-option is at the upper right of the PubMed page. You first have to create a free account.

To activate or create filters, go to [1] My NCBI and click on [2] Search Filters.

Since our purpose is to make filters for PubMed, choose [3] PubMed from the list of NCBI-databases.

Under Frequently Requested Filters you find the most popular Limit options. You can choose any of the optional filters for future use. This works faster than searching for the appropriate limit each time. You can for instance use the filter for humans to exclude animals studies.

The Filters we are going to use are under “Browse Filters”, Subcategory Properties….

….. under Clinical Queries (Domains, i.e. therapy) and Subsets (Systematic Review Filters)

You can choose any filter you like. I choose the Systematic Review Filter (under Subsets) and the Therapy/Narrow Filter under  Clinical Queries.

In addition you can add custom filters. For instance you might want to add a sensitive Cochrane RCT filter, if you perform broad searches. Click Custom Filters, give the filter a name and copy/paste the search string you want to use as filter.

Control via “Run Filter” if the Filter works (the number of hits are shown) and SAVE the filter.

Next you have to activate the filters you want to use. Note there is a limit of five 15 filters (including custom filters) that can be selected and listed in My Filters. [edited: July 5th, hattip Tanya Feddern-Bekcan]

Under  My Filters you now see the Filters you have chosen or created.

From now on I can use these filters to limit my search. So lets go to my original search in “Advanced Search”. Unfiltered, search #3 (hirsutism  AND spironolactone) has 197 hits.

When you click on the number of hits you arrive at the results page.
At the right are the filters with the number of results of your search combined with these filters (between brackets).

When you click at the Systematic Reviews link you see the 11 results, most of them very relevant. Filters (except the Custom Filters) can be appended to the search (and thus saved) by clicking the yellow + button.

Each time you do a search (and you’re logged in into My NCBI)  the filtered results are automatically shown at the right.

Clinical Queries zijn vaak handig als je evidence zoekt of een CAT (Critical Appraised Topic) maakt. In de nieuwe versie van PubMed zijn de Clinical Queries echter moeilijker te vinden. Daarom is het handig om bepaalde ‘Clinical Queries’ op te nemen in ‘My NCBI’. Deze queries bevinden zich onder Browse Filters (mogelijkheid onder Search Filters)

Het is ook mogelijk speciale zoekfilters te creëeren, zoals b.v. het Cochrane highly sensitive filter voor RCT’s. Dit kan onder Custom Filters.

Controleer wel via ‘Run Filter” of het filter werkt en sla het daarna op.

Daarna moet je het filter nog activeren door het hokje aan te vinken. Dus je zou alle filters van de ‘Clinical study category’ kunnen opnemen en deze afhankelijk van het domein van de vraag kunnen activeren.

Zo heb je altijd alle filters bij de hand. De resultaten worden automatisch getoond (aan de rechterkant).

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Twitter’s #FollowFriday #FF – Over the Top. Literally

11 09 2009

Last Update: Sunday (2009-13-09), text added in blue

The Twittermeme #FollowFriday (or #FF) was started January this year by Micah Baldwin (@micah) with one single Tweet: I am starting Follow Fridays. Every Friday, suggest a person to follow, and everyone follow him/her. Today its @fancyjeffrey & @w1redone.”

10-9-2009 23-33-49 followfriday

A friend of Micah suggested to add the hashtag (a community driven tag) #FollowFriday to the tweet, some other friends helped to spread the word and a tweetmeme was born: now, all over the world #FollowFriday is a Twitter “trending topic” on Fridays (see Mashable)

The concept of FollowFriday is that every Friday you recommend a few people to your Twitter-followers. For at least 2 reasons:

  1. it is a way to acknowledge those particular people
  2. it is a very efficient way for your followers to find other interesting Twitter people

Ideally (at least IMHO) the #FollowFriday tweets (message of 140 characters or less):

  • should consist of:
    • the hashtag #FollowFriday,  #FF or both
    • 1-3 names of people you would like to recommend (the tweet should not start with their names, because otherwise only the recommend person himself and your mutual friends will be able to read the tweet, -this doesn’t make much sense)
    • a short explanation why you recommend him/her.
  • are tweeted on Fridays
  • are more or less unique (just one or two tweets, not dozens in a row)
  • should only recommend the best people in a particular field

Two examples, one by me and one by @jpardopardo (it was my one and only #FF recommendation in two weeks)

  1. Laika (Jacqueline)
    laikas My #followfriday goes to @aarontay , a techy librarian from Singapore. Has many tips as a tweeter and a blogger #ff #fb
  2. Jordi Pardo Pardo
    jpardopardo #followfriday Cochrane tweets you can not miss: @cochranecollab @radagabriel @MESOttawa @laikas @TSC_OH @DavidTovey

this quote was brought to you by quoteurl

In these examples the hashtag #FollowFriday is followed by one or several names with the reason one should follow the person.

The general format thus would be:

#followfriday #FF @username Reason why you should follow him/her, area of interest, Their website URL, if applicable

If my followers see that I consider @aarontay a great techy librarian having a lot of good tips, they might find it worth while 2 check him by clicking @aarontay or the link to his blog If they go to his Twitter homepage and  find his tweets awesome, than they might decide to start following him.

If you’re interested in the Cochrane Collaboration, then you might try the tweople that are recommended by @jpardopardo. It takes somewhat more time, however, to check all 6 people, but it may yield some interesting new people to follow.

Thus, in principle #FollowFriday is a great tool to find other interesting people, BUT…

…suppose you’re following someone that tweets all this (x 3-5 times) every Friday?

29-8-2009 15-19-18 #followfriday

I don’t follow this person (name not shown), but if I did, these #FollowFridays are really meaningless. I don’t know why I should follow the “suggested” people, nor do I want to try all the links. Furthermore if someone produces 10 or more of these kinds of tweets (those people exist!), my twitter account gets clogged with useless clutter. Its worse than an inbox full with spam.

But some people are even worse. They not only tweet a huge amount of meaningless FollowFridays, they also retweet (RT) the FollowFridays in which they are included to let the world know how popular they are (I can’t think of any other reason than that they want to show off).

29-8-2009 15-22-28 ff dr sg

And it is counterproductive….

Instead of following the recommended people I will unfollow those kind of FollowFridaying people (at the end).

I’m not a CEO or a marketing woman. I don’t want 10000 people to follow me, and even less so do I want to follow 10.000 people back.

I only desire to follow interesting people with a high signal to noise ratio of tweets in a manageable way.

I always thought that I was exceptional in thinking like this, but last two weeks several of my Twitter friends started to talk about the downside of FollowFridays. And when I Googled, o dear, the whole Twitterverse seemed to have written about it. (glad I Googled after I had almost finished this post)

  1. Ves Dimov, M.D.
    DrVes I don’t participate in “Follow Friday” (any day is good to recommend somebody) but @Dr_Steve_Ponder offers great diabetes info as Dr/patient
  2. David Bradley
    sciencebase I think it’s time to abandon #FollowFriday as a twitter meme, unless we can make it more useful and effective.
  3. novo|seek
    novoseek agree / RT @sciencebase: I think it’s time to abandon #FollowFriday as a twitter meme, unless we can make it more useful and effective.
  4. Laika (Jacqueline)
    laikas RT @sciencebase: think it’s time 2 abandon #FollowFriday as a twitter meme, unless we can make it more useful/effective. wouldn’t agree more
  5. Walter van den Broek
    DrShock RT @laikas: RT @sciencebase: think it’s time 2 abandon #FollowFriday what about #rec?

this quote was brought to you by quoteurl

Oh and here is another one today (13-09)
pfanderson @laikas @wichor Yeah, I really hate it on Follow Friday when folks fill up a whole page nothing but people’s names. from web in reply to laikas

SO WHAT ARE THE SOLUTIONS? (blue added after publication)


  1. Abandon FollowFriday
  2. Just recommend anyone (special) whenever you like (DrVes , DrShock),
  3. @MarilynMann: “What I do find useful is when someone joins twitter and people tweet “please welcome ___ to twitter,” which can be done any day of the week”
  4. @sciencebase: “RT is the much better way to show fellow twitters that you care. If you’re RT’ing their tweets then you’re demonstrating that what they’re saying bears repeating, so recommending them indirectly…”
  5. @philbaumann ‘s tip mentioned by @problogger in the same post Mark tweets from people you want to recommend on FollowFriday by favoriting them and tweet the URL of your favorites page (i.e., see the URL of Philbaumann’s Favorites page).
  6. Share Groups of Twitter Users in One Click with TweepML (Mashable) – here are some lists from which you can choose:, including a top librarianlist. Of course there are already many lists and directories around, but the good thing is that you can personalize your own top groups and that another person can add anyone from that list by simple clicking.
  7. Use #MrTweet Instead of #FollowFriday, send your weekly recommendation there, get an overview of the most awesome people according to your friends and get recommended yourselves (see bkmacdaddy). [added 2009-09-02]


  8. Use FollowFriday sparingly and wisely, i.e. as described above. In fact the founder of FollowFriday proposes similar rules.
  9. Mention a series of people on Twitter and tell why they’re great people on your blog there is more room there (sucomments)
  10. @problogger: (on his blog your tweets throughout the day via scheduling services like Tweetlater (currently rebranding themselves as SocialOomph, Futuretweet or Hootsuite” (while taking care of the twitteretiquette, see above).
  11. Matt Stratton proposes to use the hashtag fussy-follow-friday, to discrimate good tweets from bad ones.
  12. Maija Haavisto, again on “ask others for recommendations (such as “female sports bloggers” ..), either as a normal tweet or by posing a question to someone. They reply with names of Twitter users – preceding the initial @ with a period or something else, if they want others to see their recommendations. All tweets should be tagged with #ff or #followfriday, of course.

    EXTRA TIP TO KEEP YOUR Followfriday-recommendations

  13. Perform a Twittersearch with (your @twittername  OR your twittername) (#followfriday OR #ff OR followfriday) and take an RSS-feed to that search. You see your recommendations and who has recommended you.
    Thus my search looks like
    (laikas OR @laikas)(#followfriday OR #ff OR followfriday) (and you can also add “friday”)

To add fussy-follow-friday to the follow friday tweet [10] seems unnecessarily complex to me. Asking others for recommendations [11] is a good suggestion, but I don’t see me applying that approach each Friday. I would (and already do) use this approach on selected occasions. Why not just use FollowFriday as it was meant to be used: recommend one or two people once a week [3]. I still like the idea. Contrary to marketing people and strategists, I’m already happy and honored when I’m FollowFridayed: for me it doesn’t have to lead to tons of followers (for others this is the main goal). In my case it has lead to some new, great twitterfriends. Quality is more important to me than quantity. I’ve  “met” some new interesting people, who I might not have met otherwise.

Option 2, 3 and 4 also seem very sensible to me. I share the mild) critique of @problogger regarding 5: “Not every tweet I Favorite comes from someone I necessarily want to recommend and favorites are not necessarily tweets planned on sharing. But people not using favorites often might find this an excellent option.”

6 seems more of an adjunct, nice tool, but less personal.

What do you think?

(Solutions may be added to the above list)

suggest a list of people they followed whom they believed others would also enjoy

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10 + 1 PubMed Tips for Residents (and their Instructors)

30 06 2009

The next Grand Round, the weekly rotating carnival featuring the best medical blog posts, will be hosted by Edwin Leap, a practicing emergency physician. Because the  Grand Rounds are on June 30 -one day before July 1st, which is the traditional start of that thrilling and harrowing journey called ‘residency,’- Edwin decided to make the following theme: ‘What would you like to say to future physicians?’

I’m sure doctors will give plenty advice on the skills that are most important (i.e., see here). But what advice can I give them? I’m not a doctor. I could give them some examples of “how not to behave”, but I’m sure that will be covered well by fellow patients, and probably also by blogging nurses (i.e. see the perfect Intern Survival Guide by Mother Jones RN).

So I will stay with my expertise: searching. And to make it workable, I will restrict myself to PubMed, the platform that offers free  access to 18 million citations from MEDLINE and other life science journals. 18 million, that is a tremendous amount of literature! And that is one of the main problems: the sheer amount makes it very difficult to “pick the needle from the haystack”.

Of course, literature searching is not a primary skill for doctors. It is far more important that a doctor is knowledgeable, handy, and a good communicator (!). But at one time or another, he/she has to look things up or wants to check whether current practice is the best way. Or at the very least, doctors have to stay on top of the best and  latest information. And that’s when they need to search for medical information.

Below are some tips for beginning as well as more advanced PubMed searchers. Obviously, these are only tips, this post is no tutorial and I give but a few elaborate examples. However, I plan to show entire searches in future posts. Perhaps you can help me by sending in examples or asking questions/propose cases.

Here are the 10 PubMed tips:

Tip 1 : Look before you leap.
Before even thinking of going to the PubMed site, consider whether this is the most obvious source to begin with. First decide whether you have a back- or a foreground question.
A background question asks for general knowledge and/or “facts” (questions often starting with who, what, when, why, which). “How can one  diagnose appendicitis?” “Which treatments are available for advanced prostate cancer?”  These questions can be best answered by textbooks, handbooks and certain databases. UpToDate is usually perfect for these kind of questions. A question like “Which dose of drug X should I prescribe to a woman of 65 kg with disease Y.” looks very specific, but can generally be looked up in a Pharmacopeia (“general knowledge”). Some subjects are covered by in-house protocols or specialist guidelines.

When you have a foreground question it is often better (especially for those “new” to the subject) to search evidence in aggregate or pre-filtered resources, like National Guideline Clearinghouse, the TRIP-database and/or the Cochrane Library. This will save you time, because it lowers the number needed to read: individual studies have been sought, selected, appraised and summarized for you.

Besides PubMed there are also so called 3rd party Pubmed/MEDLINE tools, which can be handy for certain questions or approaches. I’m in the middle of writing about these tools, so keep in touch. Meanwhile you may want to read an excellent overview of many of these tools and more on the blog of Mike Cadogan: Medical search for physicians. Earlier I also wrote about the handy use of PubReminer and GoPubMed to analyze text words and MeSH-terms.

Although very useful and intuitive, most of these 3rd party PubMed tools don’t have the power of PubMed and are not suitable for elaborate searches.

Tip 2: A review article from PubMed.
PubMed can be useful for quickly finding good reviews.
Below is one such example. A few months ago, Bertalan Mesko (intern then) asked advice on twitter, because his professor had difficulties finding the cause of recurrent acute pancreatitis in a young adult. Considering this was a background question- I just did a quick and dirty search as follows:

  • Go to PubMed:
  • Type acute pancreatitis in the search bar (pancreatitis may be ‘safer’, but will yield more results).
  • Click the Limits Tab and tick off the following options:
    • Facultative: Links to free full text (if you have no subscriptions/access to the medical library)
    • Facultative: Added to PubMed in the last 5 years (or read the first few hits)
    • Subsets: Core Clinical Journals
    • Type of article: Review
    • Tag-Terms: Title.
  • Click: Go

28-6-2009 12-12-32 PubMed acute pancreatitis  sonder language restr

So you search for acute pancreatitis in the title of review articles in core (English) clinical Journals. There are just 28 results in the last 5 years, including reviews in the Lancet and NEJM.

28-6-2009 12-38-45 results acute pancreatitis kort 4 vd 28 its

The Lancet review gives me a good suggestion:

In most patients, acute pancreatitis is caused by gallstone obstruction or alcohol, and no genetic testing is needed. However, unexplained recurrent acute pancreatitis might be associated with known genetic mutations in the cationic trypsinogen gene protease serine 1 (PRSS1), SPINK1, or CFTR. Mutations in the PRSS1 gene are seen in most patients with hereditary pancreatitis. In the most frequent mutations, the function of trypsinogen is increased, causing premature enzyme activation and autolysis of acinar cells.

Note that I didn’t limit on age and I didn’t add recurrent to the search, as I’m looking for a review that may discuss many forms of this disease in all age categories. Recurrent or young adult may not be mentioned in the abstract (nor in the MeSH), so I may miss important overviews if I add these terms to my search.

If you get to many hits,  you may always narrow the search later.

A similar approach has been used by drW to search for review articles on heparin induced thrombocytopenia (part 1 and 2).

UpToDate is a good source as well, ..and clinical experience. Ves Dimov responded on Twitter that he had described a similar hereditary pancreatitis case on his blog.

Note that at the end of the summer Limits will be under the Advanced Search.#

Tip 3: PubMed is just one NCBI-database.
As you may infer from the official web address of PubMed:, PubMed is (just) one of the (freely available) databases of NCBI (National Center for Biotechnology Information) . If you click on the NCBI-logo (in PubMed) you reach the Entrez cross-database search page. Most databases are particularly suited for genetics, genomics and proteomics. Several of the residents I know are also involved in research  and may make ample use of GEO (gene expression database) and/or other databases.

If you type for instance acute pancreatitis in the search bar, you see the hits per database, including the PubMed and MeSH database. In this case OMIM seems the most interesting of the genetic databases. OMIM is the “Online Mendelian Inheritance in Man” database. It contains full-text, referenced overviews with information on all known Mendelian disorders and over 12,000 genes and is intended for intended for use primarily by physicians and researchers.

29-6-2009 2-58-03 NCBI acute pancreatitis

There are 33 hits in OMIM which we could limit further (using the Limit Tab) to for instance the chromosome. Hits 3-5 describes the genes mentioned in the Lancet review and gives references to relevant studies. There is even an overview of labs performing certain tests (see for instance here)

29-6-2009 23-53-39 OMIM 12

In stead of going to Entrez, you can also directly search OMIM from the PubMed database (see Figure)

30-6-2009 11-18-27 3x oMIM

Tip 4: Looking up Citations
One of the recent alterations to PubMed is that you can just type the title in PubMed’s search bar to find a specific article. You can also type in other specifications or an entire reference. But it doesn’t always work. When you type Lancet acute pancreatitis you get too many papers (if you would look for a primary study) but if you copy the following reference from Google: Frossard JL, et al. Acute pancreatitis Lancet 2008; 371(9607) you will get zero results. This is because different Journals have different reference-styles (order, initials, punctuation) and people often make mistakes while citing.

Another possibility, much loved by librarians because of its versatility, is the Single Citation Matcher in the blue side bar. # You can fill in any field you like and some fields like “author name” have an auto-fill function.

In this case I searched for the “Author name” Frossard JL (tick “only first author“) and the First page: 143.

I get exactly 1 paper: the correct one.

30-6-2009 1-06-47 SCM

Tip 5: Saving your search and making alerts: RSS and MyNCBI
It is important for a doctor to keep up with the new developments in your field. There are ample possibilities in PubMed. One is RSS. A previous post descibes how it can be created in PubMed.

Another possibility is MyNCBI. Old fashioned? Not at all. In PubMed, I find it more useful and easier than RSS. You can find MyNCBI at the upper right or in the blue side bar#.

You have to create a free account. Once you do that, you can save searches (single searches or set numbers, but NOT the entire Search History). You can immediately save a search after performing it [1] or you can left-click the set number in the History, in this case #14, and a pop-up with options appears [2]

30-6-2009 2-02-47 save search

Searches can be saved, and executed/adapted at later timepoints or can be used to create an alert. Alerts can be mailed at any frequency you like. If searches overlap it is good to combine them, so you don’t read the same items twice (or more).

Other possibilities are: “Save Collections” (individual articles), make filters (see Tip 1) and share them.
The Save function also works in some other NCBI-databases

A nice gadget: under preferences, you can activate a highlight function: When logged in, the terms you search for are highlighted in the desired colors. That’s why acute pancreatitis and review are highlighted yellow in the PubMed search shown above.

For more information see the FAQ

30-6-2009 2-45-29 myncbi2

Tip 6: Stop Googling PubMed: why you find too much or too little

O.k. this is something you may not want to give up, because you’re from the Internet generation and you’re used to intuitive interfaces and searching by trial and error. You’re used to just take a glance at the first few hits out of thousands of records ranked by “relevance” , that exactly match the terms you entered.

This is not what you should aim for in PubMed: finding a paper because the authors use exactly the same words as you search for – and looking at the first few hits (there is no ranking in PubMed, hits are shown chronologically) do not necessarily mean it is the most relevant to you.  It only matters if the study answers your question (for your particular patient), and if it is of good quality.

Thus, don’t aim for wording similarities, aim to find the papers that provide you (and your particular patient) with the best answer.

How do you do that?

It depends on your question, but generally speaking it is not the best thing to type a whole sentence or the entire PICO in the search bar.

Usually it is best to search per term and start with the most important term first and leave out the terms that do not really matter.

So how would you search for the following question?

Does  spironolactone (anti-androgenic) effectively reduce hirsutism in a female with PCOS? Is it safe and is it comparable to Cyproterone acetate?

Some people type: PCOS hirsutism spironolactone treatment cyproterone acetate and add gender and age as well. This yields a few results which are on the topic, but yet you may miss the most relevant ones.

A better way is to search for the two most important concepts: hirsutism AND spironolactone and to look for systematic reviews and RCT’s because these provide the best evidence (see TIP 9). If necessary PCOS can be added afterwards.

Treatment is usually a superfluous term. It is (usually) better to look for RCT’s or -second best “cohort studies” (because these are the best study designs assessing effectiveness of interventions). Also take care not to apply unnecessary limits.

Always ask yourself: is this word crucial? And does adding this word/limit reduce the chance that I find a relevant paper?

Tip 7: Use Details to see how PubMed interpreted (mapped) your search

Whether you use tip 6 or not, at the very least, check the translation of your search by clicking the Details Tab. Yes, your search is interpreted or ‘mapped’, didn’t you know? That is usually a good thing, because PubMed’s keywords (MeSH) are automatically found, if you use terms that PubMed considers as synonyms for certain MeSH. This can enhance your search, but sometimes the translation is either wrong or you didn’t use the correct word (according to PubMed).

So if you check the search PCOS hirsutism spironolactone you will see that hirsutism and spironolactone are correctly mapped to a MeSH, whereas PCOS is not. Seeing this you must be alarmed, because it is very likely that there is a MeSH for such a common disease. The correct MeSH is polycystic ovary syndrome. But in this case you might as well leave PCOS from the search.

30-6-2009 4-05-06 details pcosSometimes your term is wrongly translated. If you search for (early) mobilization (of patients), PubMed will translate this as: “metabolism”[MeSH Terms] (as well as “metabolism”[subheading], that is a qualification of a MeSH term). You can imagine that this may easily result in many irrelevant papers. Rather you should use MesH terms like early ambulation and/or the opposite: immobilization. (How, I will tell you in advanced Tips, to be published later)

By taking ONE second to check Details you become aware of wrong translations and can do something about it. Exclude the term or modify the search. Or you can see that the translation is ok and leave it like that.

Tip 8: MeSH or textwords?

There are people who merely use MeSH and people who swear by textwords. I use them both.

MeSH are keywords, added by indexers to the record. It would be a pity if you would miss relevant MeSH-terms, because this will inevitably lead to missing relevant articles.

MeSH are incredibly useful for finding a group of diseases. Suppose I would like to search for the usefulness of exercise to lessen fatigue in cancer patients (no matter which cancer). If I just type cancer in the search bar, this term is not only translated into the MeSH neoplasms, but it is also automatically exploded, which means that all narrower terms (terms lower in hierarchy) are also searched for. Thus papers are found whether they are indexed with neoplasms, lymphoma or breast neoplasms.

On the other hand, if you use only MeSH you will miss new non-indexed papers or ‘wrongly’ indexed papers, while some terms may not even have an appropriate MeSH.

Therefore I usually use both MeSH and free textwords.

In the above example it is sufficient to search for hirsutism AND spironolactone. By checking “Details” you know you’re searching for the right MeSH as well.

If the MeSH is very different from the textwords you may search for both , thus: in case of early mobilization you may search:

early ambulation[mh] OR immobilization[mh] OR early mobili* (* means that you truncate the term and find early mobilised/mobilized, moblisation(s) etc. =Note that when you use an asterisk there is no longer any mapping with the MeSH!!).

Tip 9: Searching for Evidence: Clinical Queries or other search filters

When u search for the best evidence, Clinical Queries may be very handy. These are prefab search filters that aim to find the best evidence.

It is best to first search aggregate evidence by using the systematic review filter, which is really much broader because it also searches for reviews of clinical trials, evidence-based medicine and guidelines.

You just can type some terms in the Search box, but I prefer to make a basic search in PubMed’s main page first (to check the terms) and to fill in the set number, i.e. #9, later. (see Figure)

30-6-2009 13-33-33 Clinical Queries

30-6-2009 13-41-52 SR spironolactoneYou only get 10 very relevant hits, including one synthesis of evidence in Clinical Evidence, several Cochrane Reviews and other systematic reviews. Since these are all very recent papers you may decide to stop here.
If you like, you can check for individual trials as well by searching by Clinical Study Category (choose the default: therapy narrow and enter search #9 again). This gives 24 hits.

One word of caution: Not all filters are that good. The Systematic Review Filter and the Narrow Therapy filter are quite good for a quick search though. Tip: you can adapt the filters yourselves.

Tip 10: Search Logic (and Boolean operators)

What do you think you search for if you type: hand OR arm AND foot?

You probably mean to search for (hand or arm) AND foot, but Pubmed follows another logic, depending on the order of the words. In this case it puts (invisible) brackets round arm and foot, not hand or arm. Result: you find far more (irrelevant) articles, because you retrieve every(!) article using the word “hand” and a few extra with (arm and foot).

29-6-2009 1-44-31 hand foot pubmedYou can keep it under control by placing the brackets yourselves.
With complex searches I rather combine synonyms with OR and  concepts with AND using the history. It looks like this:

30-6-2009 14-17-36 foot arm history(when you don’t add operators PubMed uses the AND-operator, thus #8 #9 means #8 AND #9).
You can add another term to the search as well, or apply a clinical query or limit. The final search you can save in MyNCBI. It shows the search with the appropriate brackets when you execute it.

Besides OR (synonyms) and AND (narrowing) you have the boolean operator NOT.

Please, generally do not use NOT to get rid of articles that are irrelevant, but rather try to select positively. Why? Because by using NOT you might exclude relevant articles.

Suppose you want to find articles about nosebleeds in children by using NOT adults. Then you also exclude articles about adults AND children.

NOT can be very handy however to subtract searches from each other. Suppose you have screened 100 articles (#1) and you get a brilliant idea using another word, which gives set #5. You can go through 120 articles, but you can also subtract the two searches from each other: #5 NOT #1 : and you only have to check 20 instead of 12o records.

Extra Tip (10+1): Use your library and librarian

As an extra tip, this final and probably most useful tip.
Follow library courses if you didn’t do so already during our internship, ask your librarian to help setting up a search for an automatic alert and to deduplicate results from different databases (i.e. MEDLINE and EMBASE) and ask the help of your librarian if you want to perform exhaustive or difficult searches or if you just want some advice. It is no shame to go to your librarian. We’re there for you.

Let me end with a statement of a fellow librarian (Suzanne Bakker, freely adapted):

“Doctors learn what a Hb-test is, but that doesn’t mean that they have to do the lab test themselves, each time a patient needs a test?! The same applies to searching. It is good that doctors learn the basic stuff, and understand some pitfalls, but they need not become information specialist”

You don’t need to become an information specialist to become a very good doctor…


#Some functionalities may move from the current page (tabs and blue side bar) to the advanced search this summer

Note: Thanks to Edwin Leap who had the patience to await my post, while it was going out of hand and getting much bigger than intended…

Locate Your Visitors (2)

30 09 2008

A few months ago I blogged about how to use ClustrMaps for locating your visitors (see here). I still use Clustrmaps.
The map is cumulative: you get an overview of where the visits cluster (depicted as large or small clusters, see below) and an approximate idea of the locations. Approximate, because you can’t zoom in or look up locations.

Recently I put a new free widget in the side bar, Flagcounter. This tool also gives a cumulative overview, but it summarizes the counts per country, visualized with flags. Judging from the number of clicks on my Flagcounter, the flags seem popular. You can easily set your preferences (colors, number of colums and flags) and change it afterwards in your widget, i.e. by changing columns=2/maxflags=20 (2 columns with 10 flags) into columns=3/maxflags=21 (3 columns with 7 flags each). Please, consult the FlagCounter-Faq for these and other tips (to avoid starting over again and loose your gathered info).
It is cumulative, thus you get an idea of the countries of origin of visitors over time. On basis of the counts since September it can be concluded that I’m mostly visited (this month) by English speaking people from the United States, UK, Canada and Australia. Of course I’m also frequented by Dutch speaking people form the Netherlands and Belgium.
It is also nice to see from which exotic places visitors found their way to this blog: Libyan Arab Jamahiriya, Aruba, Vietnam, Belarus. Also surprising that India, Phillipines and Malaysia are in the top 15 this month, rather than Spain and Denmark for instance.
Who’s Among us
Also visually attractive is the “who is among us button”. It shows how many visitors are simultaneously present at your blog (within a time span of 10 minutes). The highest number I’ve seen on my blog is 7. For some blogs it’s ‘as usual’, for this blog it is exceptional.
While writing this post I found a bunch of other possibilities hidden behind this button, for instance a world map showing the locations of your visitors, with the people who are currently on your site blinking (light blue in the picture below; not working as a widget in WordPress, but visible when you click on the counter). In addition there is a map with statistics per hour, day, month, year. However, I don’t grasp what the numbers actually stand for. These certainly don’t represent a cumulative number per hour. (if I have 4 visitors per 10 min. than I don’t expect a maximum of 3 per hour or 3 per day?).
Many of these functionalities are also present in the Sitemeter, a widget that is inconspicuously present at this blog’s sidebar, but is most frequently consulted by me together with the WordPress stats.
Similar to “who’s among us” there is a nice world map, with the most recent visit in red, the last 2-10 visits in green and the other 90 visits in white. You can zoom in, look at the exact location and ip-number of the visitor whether in day or night zone.
It is a good way to improve your topographical knowledge. 😉
Alas you can only observe the last 100 visitors, which means that in my top days I loose the statistics within a day.
If you want to upgrade, you have to pay a few dollar per month at least.

What I like the most, besides the map: the Visit Details of last 100 visitors. This list shows visit time, visit length (however if someone is just reading the frontpage without clicking, it counts as zero seconds), number of pages visited, entry and last page, IP address etc.
You can exclude your own visits and make the stats public or privat.
By using the sitemeter (in conjunction with the WordPress stats) you get an impression which visitors visit which pages.
Sitemeter has helped me to identify the IP address and domain of someone sending me a Google Doc invitation that was really meant as spam (I found out because that IP linked to someone referred to at my blog and later found in that Google doc (see earlier post)).
The sitemeter also helped me to identify the 10.000st visitor: Wowter from Wageningen.

Finding assigned MeSH terms and more: PubReMiner

24 09 2008

Generally when searching PubMed I use both MeSH and textwords. If you already have some nice articles, either by performing a quick and dirty search or looking at the Related Articles or your colleague gave you one or two, then you can find the MeSH assigned to these papers by looking in citation format (see Fig). However going through a set of articles looking at all indexed terms takes quite some time and one doesn’t easily get an overview of the overall frequency of MeSH in a set of records.

Therefore, Rachel Walden, asked first at Twitter and then at David Rothman’ site (see here):

“What I’d like to do is to be able to enter the PMIDs of several citations and have the tool search MEDLINE via PubMed for the assigned MeSH terms, and return a single list of the terms used by any of the entered citations with a measurement of frequency. For example, if I input PMIDs 16234728, 15674923, and 17443536, the tool would return results telling me that 100% or 3 of 3 use the term “Catheters, Indwelling”, 2 of 3 use “Time Factors,” 1 of the 3 uses “Urination Disorders,” and so on. Although this example uses 3 PMIDs, I’d like to be able to input at least 10, just based on personal experience.”
(PMID is the unique PubMed-identifier, by searching for the PMID you get one specific record.)

The suggestions made by several people were summarized by David in another post (see here). The following 3rd-party PubMed/MEDLINE tools seemed most promising:

Both give useful results. The layout- is very user-friendly.

I tried my own sets of 20 PMIDs (from a systematic review search on “predictive models for in vitro fertilization”) and http://www.docmobi gave this result:

This was the result when I selected: “primary terms only“. If I selected “include secondary terms” I would find female and pregnancy as well, but at rates of 175%. First I didn’t understand, but than I realized that pregnancy occurs as “Pregnancy” and as “Pregnancy Rate”, whereas female occurs as “Female” and as “Infertility, Female”. Therefore Pregnancy and Female can occur more than once as single words in one record (thus accounting for >100%). It is odd however, that important MeSH like Pregnancy and Female (which are really check tags, MeSH that should be assigned to each article that is about pregnancy or females) are not included in the primary list.

Although useful and nicely presented I still not find this ideal:

  • Check tags are not in the list (with “primary terms only”)
  • Publication types and substance names are in neither list.
  • Subheadings are not included. For words not captured by a single MeSH, but by a combination of MeSH and subheadings this is especially important.
    For instance, there is no MeSH for EGFR-inhibitors, you have to use:
    Receptor, Epidermal Growth Factor/antagonists & inhibitors
  • Textwords are not included (not on Rachel’s wish list but on mine)
  • Personally I find the list so simple that I would have find the terms immediately myself.

Coincidentally I found another 3rd party tool which does the job much better (I think): (PubMed) PubReminer, produced by Jan Koster at our hospital (AMC, Amsterdam). I looked at it, because my colleagues and I are going to discuss it today.

This is the procedure I would recommend to find assigned MeSH and more, starting from PubMed (which is not absolutely required, because you can also search directly in PubReMiner)

  • Collect the PMID’s of the papers you want to analyze.
  • If you have selected the papers in PubMed (on the Clipboard, in My collections or in your search set) then Set the Display Tab on: UI List (Unique identifiers), and export these PMID to a textfile by choosing “to Text” from the Send to button. You get a simple list of PMID’s that you can copy/paste to PubReMiner. (not required, but handy)

  • Go to PubReminer, paste the PMID’s in the search box as one string.
  • Click: “Start PubReminer” (if you enter a search you may wish to apply limits)

  • You see the results, in the following columns: Year, Author, Journal, (Text)Word, MeSH, Substance, Country.

  • Afterwards you can decide
    – Whether the words are searched in title only, in Title and Abstract or also in MeSH and RN.
    – which columns are displayed
    – whether similar words are merged or not.
  • You can use selected terms to build up your search in PubReMiner and/or
  • You can export the terms as a text-file (!)
  • And, by the way, you can download a plugin for IE or Firefox (Fig. right)

This tool is not very intuitive, but the result is quite ideal.

You get both a list of MeSH plus their subheadings AND a list of textwords (and substance names).

There are very useful terms in here, for instance logistic models[mesh]. As textwords I should use ‘logistic regression’ or ‘multivariate analysis’. It is only a pity that terms are given individually and spread: the context is lost (‘analysis’ may for instance be too broad, it is only useful in combination with ‘multivariate’ or ‘regression’).
With respect to the MeSH only individual terms are given. ‘Pregnancy’ is mentioned 19 out of 20 times in the MeSH-list. Does this mean I’m missing one paper by searching “Pregnancy”[MesH]? No, because that paper is indexed with the narrower term “Pregnancy Rate”. (and you will find it by exploding Pregnancy).
So the context and the hierarchy are lost here as well.

But it is about as good as one can get.

I’m gonna use this tool (as an adjunct at least). Seems that it has some other potentials as well: look up genes, find the research interest of an author, determine the journal to submit your work to. Well I probably learn that in a few hours. Perhaps I will come back to it later.