Way behind on sharing this, but better late than never.
The Mayo Clinic Libraries’ Liblog has a screencast by Melissa Rethlefsen on PubMed’s new Advanced Search features that you can embed on your own page:
In case I have not mentioned it recently: Melissa is awesome.
The Yale Image Finder searches PubMed Central articles for images.
That sounded not only like a good idea, but a good idea I’d heard before. In July of 2007, I posted about UC Berkeley’s BioText1, which seems to already search PubMed Central for images. Why build another tool to do the same thing?
The answer is found in this Bioinformatics article
The authors note they are aware of BioText, but that…
…we are not aware of a biomedical search engine that can retrieve images by searching the text within biomedical images. This offers several advantages over searching over captions alone. First, captions may not contain all the textual information that is contained in the images. Second, image texts are usually very specific, allowing for precise matching of images with related images.
Neat. So Yale’s tool does OCR on text appearing in images and adds that text to its searching index of images in PubMed Central. Cool.
So the book is getting some attention!
Internet Cool Tools for Physicians is in Google Book Search
Stephen Francoeur made this little video:
The Mid-Atlantic Chapter of the MLA mentioned it on their blog.
The MLA’s Taskforce on Social Networking Software posted about it, calling it “…an accessible, illustrated and contemporary guide to online tools in medicine.”
Laika, whose blog has quickly become one of my favorite MedLib blogs, mentioned it, as did Creaky.
I’m watching WorldCat.org with interest to see which libraries are getting it (though Duke’s copy doesn’t show up yet).
Dr. Shock (MD, PhD) gave it a very nice review.
I’m lucky to count as friends people like Meredith Farkas and Michael Stephens, both of whom thought the book worthy of mention on their very popular blogs.
Gosh- Brandi blogged about it way back in August- well before it as released!
I’m pleased to see mention of it in languages other than English.
The President and CEO of Community General Hospital blogged about it.
It has gotten some buzz on Twitter.
We’re anxious to hear any feedback you have about the book- please let us know what you think….and what you think needs to be added or changed for the second edition! 
Got my hands on my copies of the book today! How exciting!
Yay!
You can buy a copy from:
Springer Publishing
or here:
I’m looking forward to eventually seeing it in WorldCat.
Congratulations to Melissa Rethlefsen (who wrote a heck of a lot more than I did)! You should really go email Melissa now and tell her how much she rocks.
I frequently like to listen to Uncontrolled Vocabulary, an LIS call-in talk show podcast run by Greg Schwartz.
When he posts each episode, Greg also posts a list of the show’s participants and summary of what was discussed- and that makes the podcast somewhat searchable. If one wants to know when gaming has been discussed, one can use the search field in the right sidebar and get results like these which show three episodes Greg noted as having discussed gaming.
The Problem:
Greg puts a lot of time and effort into Uncontrolled Vocabulary, but itd be much more searchable if Greg transcribed every episode and made that that transcription available for searching. It’d be even cooler if Greg indexed the transcription against timestamps in the audio files so we could jump to the point in the audio where a particular search term is spoken. However, Greg has a job, a family, and a life- so that’s just not a reasonable thing to suggest he do.
EveryZing as the Solution:
Fortunately, EveryZing is already doing it for him.
EveryZing machine-transcribes each episode of Uncontrolled Vocabulary and lets you search that transcript. When it finds your search terms, it links you to the moment in the audio where the search term is spoken.
This link will take you to EveryZing’s index of Uncontrolled Vocabulary episodes. From here you can search not just Greg’s notes on each show, but transcripts. If we use EveryZing to search for “gaming,” we can see that it is mentioned in seven episodes
Say I want to hear the moment in Episode 50 where the phrase “gaming initiatives” appears.
All we have to do is click the hyperlinked timestamp and EverZing will load that episode in a flash player and queue it up to that moment in time. Even cooler, I can embed that player at that timestamp on a Web page…like this:
Machine transcription is far from perfect and it is entertaining to see “ALA” transcribed as “Malay,” but I’m pretty impressed by the potential of this technology.
Almost two years ago, I wrote:
Eventually, the metadata of an audio file (any audio file) should contain not just a text transcript of the audio content, but searchable transcript, indexed to minutes and seconds of the audio. Lets say you want to download the latest Library 2.0 Gang podcast specifcally because you want to hear the first thing Michael Stephens has to say on the topic du jour. You should be able to search the Podcast for the word “Stephens”, select the first first hit in the returned search results, and be taken instantly to the first moment in the audio when the word “Stephens” is spoken.
Imagine the usefulness of such a feature for a clinician attempting to find specific details in a podcast he/she has downloaded.
We’re not quite there yet. The transcription is not included in the audio file itself and the portable audio players don’t yet have the software to search it- but EveryZing shows we’re definitely closer. You can search the transcription of NEJM Interviews, JAMA Audio Commentaries, Johns Hopkins PodMed, MedlinePlus: NLM Director’s Comments and others.
Other neat features of EveryZing:
The Saab Memorial Medical Library (at the American University of Beirut) has quizzes to test your PubMed knowledge in both Basic and Advanced flavors.
Just curious: If you teach (or have taught) a class on using PubMed, are these the sorts of questions you’d use to determine how well your students absorbed the material?
Hello, this is Nikki from Eagle Dawg Blog stepping in while David enjoys time with his beautiful family. Here is a friendly reminder that a 30 minute PubMed search clinic will be offered tomorrow (July 17th) at 2pm Eastern time (what time is that throughout the world?) by the National Library of Medicine (NLM) and the National Training Center and Clearinghouse (NTCC) to the first 300 participants to log in at http://www.nlm.nih.gov/bsd/disted/clinics/pmupdate08.html Please note the following from NLM about questions you have during the search clinic:
The Chat (Q & A) Pod:
Because of the size of this clinic, we unfortunately will not be able to take questions using audio. Please use the Chat (Q & A) pod to type questions and comments to the trainers. Please enter your questions throughout the presentation, as you think of them. Your question will be visible only to you and the trainers, unless the trainers choose to display your question and an answer to all participants. The trainers will answer the questions that seem helpful to all participants verbally, at the end of the clinic. Others may be answered individually via the Chat pod. If the answer requires additional research or we run out of time, the trainers will contact you via e-mail following the clinic and/or post the responses to the clinic’s Web site.
If you have not used Adobe® Connect™ before (or since they acquired Macromedia Breeze), check that you have the most recent free Adobe Flash update (version 9.0.124) as using 9.0.115 and earlier versions may result in audio problems while accessing the archive later on. I recommend viewing the recent ‘Awakening the Searcher Within’ seminar series from the National Network of Libraries of Medicine, Pacific Northwest Region (NN/LM PNR) archive both as a way to test your audio and see how the chat pod is used in Adobe® Connect™ in addition to reviewing some great search strategies.
Check out Krafty Librarian’s post for background on what others have blogged about the Automatic Term Mapping (ATM) changes in PubMed. Some modifications to the recent ATM formatting were made on July 2nd to have the ATM not include individual word searches in all fields for multi-word substances and MeSH headings that include individual numbers or letters. I probably sound like a broken record, but please do continue sending in your concise feedback to NLM as user requests are what drive changes to their resources. Hope to see you at the search clinic!
____________
Interested in writing a guest post while David is on paternity leave? Send your submission to:
Been meaning to post about e-LiSe since I saw the article about it in March.
“e-LiSe (e-Literature Searcher) is an easy-to-use web-based application which finds biomedical information truly related to English words provided by the user. The program uses PubMed database of scientific abstracts as the source of data and a novel bio-linguistic statistical method (based on Z-score), to discover true correlations, even when they are low-frequency associations.
e-LiSe is also capable of finding names of researchers correlated to the information searched by the user. It can function as a name reference engine, answering questions like “who is working on specified subject?” or “what are the coworkers/collaborators of a certain person?”. For the latter the software uses the list of co-authors of each publication a researcher has written to display connections between scientists.”
PostGenomic’s PubMed Faceoff is the first 3rd Party PubMed/MEDLINE Tool I’ve looked at that really made me chuckle.
This site applies a simple, photorealistic variant of the Chernoff Faces visualization technique to impact factor data for papers in the PubMed database of biomedical literature.
Basically it allows you to search PubMed and have the results represented as a set of human faces.
Each paper is represented as a face. The ethnicity and gender of the face is selected at random for visual interest – you can turn this feature off if you so choose.
The age of a face correlates with the publication date of the paper. Younger faces are more recent papers.
A smile means that the paper has been cited more times than expected (based on its age). Larger smiles mean more citations.
A frown means that the paper has been cited far less than you might expect.
The raised eyebrows correlate with the impact factor (sort of – actually the Eigenfactor) of the journal in which the paper was published.
Some example search results:
I absolutely appreciate the concept (potentially being able to estimate several properties of an article at a glance)- it’s just that some of the facial expressions crack me up.
http://services.nbic.nl/cgi-bin/copub/CoPub.pl
CoPub is a text mining tool that detects co-occuring biomedical concepts in abstracts from the Medline literature database. The biomedical concepts included in CoPub are all human, mouse and rat genes, furthermore biological processes, molecular functions and cellular components from Gene Ontology, and also liver pathologies, diseases, drugs and pathways. Altogether more than 250,000 search strings are linked with CoPub.
Special attention was given to genes and proteins. For all human, mouse and rat genes not only long forms of names were used, but also their symbols and aliases, which increases recall. Symbols not referring to genes or proteins are a well known problem, but sophisticated scripts detect these homonyms and neglect the abstracts in which they occur thereby increasing precision.
Features include:
* Fast and easy access to relevant abstracts
* Single gene search in all categories
* Multiple gene search in all categories
* Single keyword search in gene category
* Categories of biomedical concepts: genes (human, mouse, rat), liver pathologies, biological processes, molecular functions, cellular components, diseases, drugs, pathways
* Use of long forms, symbols and aliases of genes
* Homonym detection
* Statistical filter to display only significant biomedical concepts
* Based on Medline abstracts till February 2008
From MiSearch Help:
“MiSearch works with NCBI Entrez and your history of browsing to build a profile of your areas of interest, and uses this information to rank citations likely to be of most most information to you at the top of the list.”
…
“MiSearch uses a classification algorithm based on MeSH term, substance names and author names associated with citations. Two sets are defined. One is the set of articles you have previously clicked on to view. The other is all of PubMed. For each citation in the retrieval set, the algorithm calculates the likelihood that the citation is a member of these two sets. Article having the highest likelihood of belonging to the set of articles you have viewed are ranked at the top of the list.
The “User” field is used as an identifier to track usage. If you do not provide a name, the IP address of your request will be used as a default. If you know you will be doing searches for different tasks with different subject areas, feel free to define a “User” for each task.”
Slides from an MLA 2008 presentation by NLM Associate Fellow Marisa Conte
Nikki Dettmar points to a 25-minute PubMed Review slides & audio presentation from the NLM Theater at MLA 2008. Nikki has a great breakdown of sections, too- in case you want to skip to particular topics.
(Nikki, where’s your MedLib Blog Badge?)
See also: Before and after comparison at the Dragonfly
Thanks again to Nikki Dettmar for the heads-up (via the Medlibs GroupTweet)!
At the AMA’s Medical Communications Conference, I insisted to a communications professional from a state professional association that professional associations needed to take advantage of social Web technologies and utilize them to the benefit of their members.
When pressed to explain WHY professional associations should do this, I said that those professional associations who don’t adopt these technologies will find that their members (and potential members) will use these technologies (without assistance from their professional associations) to organize without organizations1. Where will the professional associations be left when that happens?
With that in mind, here are some projects I’d love to see the MLA pursue.
As I understand it, books published by the MLA are generally written by uncompensated MLA members, edited by uncompensated MLA members, and selected for publication by a committee of uncompensated MLA members. The selling of these books does not raise much (if any) money for the MLA.
Since this book publishing makes no money and the MLA members are okay with donating their time, why not post the book content online in the members-only section of MLAnet and make access to them a benefit of membership? The cost of providing this content would be reduced for the MLA and the content itself would become available to (and searchable by) all members of the MLA, regardless of their institutions’ book budgets. If any members just HAVE to have an MLA book on paper, MLA can make them available for order via Lulu, shifting the cost of print copies exclusively to the reader.
Alan Fricker was the first person who put this idea in my head.
Know who writes reviews for Doody’s without compensation? Largely MLA members.
Know who makes the decision to include access to DCT in their budgets? MLA members.
Why couldn’t the MLA offer a platform that accomplishes the same thing as DCT and invite all of the Doody reviewers to instead review for the MLA? The argument for both librarians and other clinical professionals would be that, if the resource is made available to all MLA members as a benefit of membership, everyone’s libraries can be better-informed and reallocate the money that used to be spent on DCT towards other needs.
Perhaps a (free) Pligg installation in the members-only section of MLAnet would do the trick?
A handful of people I know have spent a good bit of time trying to convince me that librarians sometimes actually prefer to hoard their expertise and would be unwilling to share the hedges and filters they’ve spent time developing and perfecting. I prefer to hope that hoarding is on its way out and that the better model of unrestrained sharing will completely supplant it. With copy-and-paste ease, it’d be a pretty easy kind of wiki for librarians to contribute to- and the usefulness to working librarians (and to those who train new librarians) would be enormous.
These are just three ideas. Are they bad ideas? What else would be a good Web project for the MLA to take on? Let me know in the comments?
1
Hakia says they’re tapping the expertise of librarians. As CEO Dr. Riza C Berkan writes on the Hakia blog:
Every Web search starts with two queries. One is X. The other one is “who knows X the best?” Because finding X is not enough if the author of that page does not know X himself/herself. This will immediately resonate with you if you ever searched for medical, legal, or financial information for a serious case.
This was called the “credibility” criteria in the old world-order which has progressively vanished in the new age of Internet search engines. You enter X, and get the same “popular” perspective without distinction of credibility. You may recognize some of the sources, but are you an expert yourself about these things?
Ironically, there is a science for this. It is the science of libraries and librarians. That’s their job. They know what is credible, trustworthy, and commercially-unbiased.
So how does Hakia leverage librarian expertise? They say it is by indexing “quality sources” which are “taken from the Medical Library Association recommendations.”
That’s a great idea of where to start, but anyone could accomplish the same by making a Google CSE like this one. The Google Health Co-op greatly surpasses Hakia’s effort here by including a greater number of recommended sites and greater value from having more authoritative recommenders than just the MLA.
Also interesting is that Hakia has created a little micro-portal for each of the following sites:
PubMed – http://pubmed.hakia.com
World Health Org – http://who.hakia.com
ClinicalTrials.Gov – http://clinicaltrials.hakia.com
Centers for Disease Control – http://cdc.hakia.com
The National Cancer Institute – http://nci.hakia.com
National Heart, Lung and Blood Institute – http://nhlbi.hakia.com
Mayo Clinic – http://mayoclinic.hakia.com
familydoctor.org – http://familydoc.hakia.com
Healthfinder – http://healthfinder.hakia.com
HIV InSite – http://hivinsite.hakia.com
Kidshealth – http://kidshealth.hakia.com
Medem – http://medem.hakia.com
MEDLINEplus – http://medlineplus.hakia.com
NOAH – http://noah.hakia.com
American Cancer Society http://acs.hakia.com
Cancer Care, Inc. – http://cancercare.hakia.com
Oncolink – http://oncolink.hakia.com
Women’s Cancer Network – http://womenscancernet.hakia.com
American Diabetes Assc. – http://ada.hakia.com
diabetes123 – http://diabetes123.hakia.com
Children with Diabetes – http://childrenwithdiabetes.hakia.com
The Diabetes Monitor – http://diabetesmonitor.hakia.com
Joslin Diabetes Center – http://joslinharvard.hakia.com
National Institute of Diabetes & Digestive & Kidney Diseases – http://niddk.hakia.com
American Heart Association – http://aha.hakia.com
Congenital Heart Information Network – http://tchin.hakia.com
March of Dimes – http://marchofdimes.hakia.com
These are also interesting, but superior results could be achieved using existing tools. Rather than searching Hakia’s portal for the American Heart Association for myocardial infarction, we could more easily search Google for myocardial infarction site:americanheart.org and make use of Google’s further refinements from there.
When I posted in February about JANE, I should also have mentioned eTBLAST(previously mentioned here):
Our service is very different from PubMed. While PubMed searches for “keywords”, our search engine lets you input an entire paragraph and returns MEDLINE abstracts that are similar to it. This is something like PubMed’s “Related Articles” feature, only better because it runs on your unique set of interests. For example, input the abstract of an unpublished paper or a grant proposal into our engine, and with the touch of a button you’ll be able to find every abstract in MEDLINE dealing with your topic. No more guessing whether your set of keywords has found all the right papers. No more sorting through hundreds of papers you don’t care about to find the handful you were looking for–our search engine does it for you.
I also recently stumbled across Whatizit:
Whatizit is a text processing system that allows you to do textmining tasks on text. The tasks come defined by the pipelines in the drop down list of the above window and the text can be pasted in the text area. The description of each individual task/pipeline can be found following the link next to the submit button. Whatizit is also a Medline abstracts retrieval/search engine. Instead of providing the text by Copy&Paste, you can launch a Medline search. The abstracts that match your search critetia are retrieved and processed by a pipeline of your choice.
I have distinct childhood memories of asking my mother what one word or another meant. She would point out that there was a dictionary close at hand designed exactly for that purpose and invite me to make use of it.
I remember asking my father to teach me to program in BASIC. He cheerfully agreed and handed me the big brown manual.
So maybe I’m weird and so are my folks, but these memories inform my take on the chatter in the blogosphere and on MEDLIB-L about this post by Harvard PhD student Anna Kushnir in which she expresses her frustration with PubMed. Kushnir writes (in part):
“I hate PubMed. I hate it with a burning passion. For a site that is as vital to scientific progress as PubMed is, their search engine is shamefully bad. It’s embarrassingly, frustratingly, painfully bad.”
[...]
“Why is PubMed so behind the times? Why? How does it even work? Does it search only the abstract? Does it also search the body of the papers that are available online? Why does it get so massively confused by an author’s initials and last name together, in one search? Why can’t it alert me when papers relevant to my work are published?”
I’m the first to admit that PubMed has problems and much room for enhancement, but if Kushnir had bothered to look at PubMed’s help manual or try some of its excellent tutorials she’d have learned exactly how it works, what PubMed indexes, how she can search by author, and that it can alert the user when papers relevant to her work are published via email or RSS.
So while PubMed has real, legitimate problems, Kushnir’s complaints don’t really touch on any of them. She could’ve resolved the problems she notes by flipping through the well-written, clearly laid-out, easy-to-navigate manual.
A number of helpful people who are much nicer than I am left useful comments for Kushnir.
Medical librarian Kathleen Crea offered a clear explanation of how articles are indexed and what MeSH is.
Medical librarian Rachel Walden even offered to help remotely with specific searches if Kushnir didn’t have a Harvard medical librarian handy.
But Kushnir decided that none of this really helped and later commented:
I don’t think I should have to be, or enlist the services of, a medical librarian in order to do a simple search on a literature search engine. PubMed should be an intuitive search engine such as Google, or others. I don’t know of many researchers, either MDs or PhDs, who have had extensive training in computer science or search algorithms. I am going to go out on a limb and say that I am representative of many other biomedical researchers in my struggles with PubMed. I am trained in Cell Biology and Virology. PubMed should be tuned to my needs and my skill set. I should not have to tune to it. Harsh as it may sound, PubMed is most useful for biomedical professionals, not for medical librarians or for computer scientists. Yes, if I devoted an afternoon or more to learning the system I dare say I would become a proficient, but my question stands – why should I have to?
Huh.
The index of biomedical literature searched from PubMed is a vast and complex set of data. Any tool that will search it effectively for very specific needs will necessarily be complex. If Ms. Kushnir doubts this, perhaps she should perhaps try any other interface for the same data. Some other interfaces work better for some purposes and some users, but all are complex.
Using PubMed does not require “extensive training in computer science or search algorithms,” it requires reading the manual. Kushnir actually admits that if she “devoted an afternoon or more to learning the system” she would “become a proficient,” and yet she fails to recognize her complaints as the whining they are.
Kushnir writes at JOVE:
My rant somehow wound up on a medical librarian listserv and they came out in force defending NCBI and PubMed, listing pages and pages of helpful and warm instructions and hints on how to make it do what I need it to do, pages of suggestions, with offers of hands-on assistance and training, which have all been wonderful. Occasionally though, they were biting and harsh, saying that if I only knew what I was doing (and only if I weren’t so ignorant… yup, ignorant), PubMed would seem to me the greatest thing ever.
I’m not criticizing Kushnir’s ignorance and would take issue with those who did. Ignorance, once identified, should alert the librarian to a teaching opportunity- not an occasion for shaming. Criticizing the extraordinary laziness in her refusal to receive help from a librarian or to take a quick look at the manual, though? That’s fair game.
Kushnir continues:
I am a research scientist by long, hard training. I am a fairly web-savvy research scientist, and still, I have trouble with PubMed.
As a medical librarian friend recently pointed out to me, it requires instruction to learn to drive a car. Kushnir is unwilling to read the manual and wants to blame PubMed/NLM for her difficulties. Kushnir talks about having spent hours trying to get PubMed to do what she wants, but declines help from multiple medical librarians who’re happy to teach her and can’t be bothered to invest 30 minutes in reading from the manual because it should, in her thinking, be possible to do without any effort on her part.
Kushnir continues:
The search engine is not made for medical librarians. It’s not made for computer programmers. It’s made for scientists, to be used by scientists, needed most by scientists.
Actually, Medline’s history is that it was made primarily for medical librarians and secondarily for physicians, but that’s not really important.
It should be easy for scientists, goofy, only moderately-computer literate scientists, to use. It should be intuitive (read: Google), it should not have a ginormous page of inscrutable instructions, it should not require the hour-long training sessions, kindly offered at most medical libraries. It should be plug and chug.
I might just as well argue that the tools of virology research should be intuitive to me. After all, I’m a very computer-literate, Web-savvy biomedical information professional. Why should I need her years of training to understand her work?1
“Inscrutable?”
Kushnir also describes PubMed’s help documentation as “inscrutable.” When I was teaching myself how to use PubMed, I found the documentation clear and helpful, so this surprised me. I decided to run the PubMed Quick Start document through Google Docs’ analysis:
Let’s review these scores:
Flesch Reading Ease: 62.97
(A score from 60-69 is considered “standard”)
Flesch-Kincaid Grade Level: 5.00
(Fifth grade)
Automated Readability Index: 5.00
(Again, fifth grade)
So it would appear that the help documentation is written at a fifth-grade level. I find it hard to believe that a PhD student at Harvard cannot read at a fifth-grade level, so I’m left with the impression that Ms. Kushnir didn’t actually attempt to read any of the documentation before declaring it “inscrutable.”
Suggestions for Ms. Kushnir and other research scientists who don’t like reading the instructions:
So the tool is necessarily complex because the data it searches is complex and the user refuses to read the well-written help documentation or accept help from a friendly librarian (even when multiple librarians are reaching out across thousands of physical miles of distance and the gulf of the patron’s unwillingness to learn).
I can only conclude that sometimes the user *is* broken.2
Thank you to the two medical librarian friends who read the first draft of this post and offered comments.
1
2
Xoova is actually pretty cool in that it allows you to search “by any combination of name, specialist, treatment, condition, health plan & location.”
So if I wanted to find cardiologists in Syracuse, I could search for cardiology Syracuse, NY and get a list.
If I’m looking for a particular physician, it does pretty well with that too. If I search for Wasserman Syracuse cardiology, I’m taken to a page for Dr. Louis Wasserman that contains the contact information for his practice and a photo:
Just below that are (incomplete) lists of accepted health plans and affiliated hospitals:
Each physician’s page also has rating and recommendation features:
It also provides a definition of a cardiologist and a Google map showing where the physician’s office is:
Xoova’s FAQ seems to indicate that these profiles are actually created by the physician (or the physician’s office staff). If that’s the case, some physicians at the hospital where I work are significantly more Web-savvy than I suspected…so I’m thinking that maybe some of this information is imported from elsewhere.
Gerry McKiernan points out two Facebook applications for searching PubMed, PubFace and PubMed Search.
PubFace Results:
PubMed Search Results:
It’s sort of neat to be able to quickly share a PubMed citation with another Facebook user (see the link in the PubFace results above for “Send to a friend” or PubMed Search’s “Share this” button) and it is handy to be able to add citations to a collection (see PubFace’s “Add to MyLibrary” links or PubMed Search’s “add this to your favorites”)… but I’m having trouble seeing how it is preferable to using PubMed itself and making use of MyNCBI or “Send to email”…or using a powerful bookmarking tool like del.icio.us, Connotea or CiteULike.
I’m only a casual Facebook user, so it is entirely possible I’m missing something. If so, please clue me in? Thanks!
A few weeks ago I mentioned a post from Dr. Joshua Schwimmer about Google Book Search in which he described a time when it proved extremely useful in a clinical setting.
Google must have liked the positive exposure because they interviewed Dr. Schwimmer. The interview (just over two minutes) is embedded below.
If you’re reading this in an aggregator or via email, you may have to visit the site to view the embedded video above
Dr. Schwimmer’s blogs:
Jon Brassey writes:
I may have missed something, but none of these alternate interfaces allow easy searching of PubMed. Some are wonders of programming, some allow some very neat tricks but none make searching of PubMed easy.
That’s a fair criticism, I suppose. I think that although PubMed has come a very long way in developing tools that make searching the NLM’s databases easier for medical librarians, clinicians and consumers, it still takes some knowledge and skill to perform a really useful search of the primary literature.
Jon continues:
I suppose my biggest issue with PubMed is that doing a search of statins returns 18,491 results. Unpicking that a bit:
* Most research shows search engine users finish looking after 3 pages of results.
* From our own experience with TRIP we also know that most users only use single search terms (e.g. asthma, hypertension).So what I’m saying is that statins is a realistic search term and that suggests that 18,431 (18491-60) results are superfluous.
Therefore, the two challenges to me are:
* Return fewer results in the first place
* Allow users to easily qualify their searches.
Let’s bring Jon’s challenges to Healia’s PubMed/MEDLINE Search. It isn’t my favorite, but if Jon uses it to search for statins, he’ll see that, at the time of this writing, only 7,731 results are returned1 and that there are a number of tools for “qualifying” the search right there on the search results page.
You can adjust for date:
You can filter for review articles or for English language articles only:
You can filter by patient demographics:
Healia even recognizes that we’re searching about a class of drugs and gives us tabs so we can filter by Dosage, Usage, or Side Effects:
So let us assume that Jon’s hypothetical user wants to see English-language review articles about the dosage of statins from only the last 5 years. That’s only twelve results. I think this passes Jon’s test.
With that out of the way, I need to add:
Criticizing PubMed for returning too many search results for a query as inadequate as “statins” seems unreasonable to me. A major part of what makes PubMed an amazing tool is its complexity. I believe and hope that new tools will continue to be developed that make the data useful to various kinds of users in various new ways, but I don’t expect that getting exactly what one wants from the primary literature will ever truly be “easy.”
1
PubMed, on the other hand, translates statins into “hydroxymethylglutaryl-coa reductase inhibitors”[MeSH Terms] OR “hydroxymethylglutaryl-coa reductase inhibitors”[Pharmacological Action] OR statins[Text Word].
After all, if we search PubMed for “statins” as a string, we get only 7,990 results.
