No, Google Scholar Shouldn’t be Used Alone for Systematic Review Searching

9 07 2013

Several papers have addressed the usefulness of Google Scholar as a source for systematic review searching. Unfortunately the quality of those papers is often well below the mark.

In 2010 I already [1]  (in the words of Isla Kuhn [2]) “robustly rebutted” the Anders’ paper PubMed versus Google Scholar for Retrieving Evidence” [3] at this blog.

But earlier this year another controversial paper was published [4]:

“Is the coverage of google scholar enough to be used alone for systematic reviews?

It is one of the highly accessed papers of BMC Medical Informatics and Decision Making and has been welcomed in (for instance) the Twittosphere.

Researchers seem  to blindly accept the conclusions of the paper:

But don’t rush  and assume you can now forget about PubMed, MEDLINE, Cochrane and EMBASE for your systematic review search and just do a simple Google Scholar (GS) search instead.

You might  throw the baby out with the bath water….

… As has been immediately recognized by many librarians, either at their blogs (see blogs of Dean Giustini [5], Patricia Anderson [6] and Isla Kuhn [1]) or as direct comments to the paper (by Tuulevi OvaskaMichelle Fiander and Alison Weightman [7].

In their paper, Jean-François Gehanno et al examined whether GS was able to retrieve all the 738 original studies included in 29 Cochrane and JAMA systematic reviews.

And YES! GS had a coverage of 100%!

WOW!

All those fools at the Cochrane who do exhaustive searches in multiple databases using controlled vocabulary and a lot of synonyms when a simple search in GS could have sufficed…

But it is a logical fallacy to conclude from their findings that GS alone will suffice for SR-searching.

Firstly, as Tuulevi [7] rightly points out :

“Of course GS will find what you already know exists”

Or in the words of one of the official reviewers [8]:

What the authors show is only that if one knows what studies should be identified, then one can go to GS, search for them one by one, and find out that they are indexed. But, if a researcher already knows the studies that should be included in a systematic review, why bother to also check whether those studies are indexed in GS?

Right!

Secondly, it is also the precision that counts.

As Dean explains at his blog a 100% recall with a precision of 0,1% (and it can be worse!) means that in order to find 36 relevant papers you have to go through  ~36,700 items.

Dean:

Are the authors suggesting that researchers consider a precision level of 0.1% acceptable for the SR? Who has time to sift through that amount of information?

It is like searching for needles in a haystack.  Correction: It is like searching for particular hay stalks in a hay stack. It is very difficult to find them if they are hidden among other hay stalks. Suppose the hay stalks were all labeled (title), and I would have a powerful haystalk magnet (“title search”)  it would be a piece of cake to retrieve them. This is what we call “known item search”. But would you even consider going through the haystack and check the stalks one by one? Because that is what we have to do if we use Google Scholar as a one stop search tool for systematic reviews.

Another main point of criticism is that the authors have a grave and worrisome lack of understanding of the systematic review methodology [6] and don’t grasp the importance of the search interface and knowledge of indexing which are both integral to searching for systematic reviews.[7]

One wonders why the paper even passed the peer review, as one of the two reviewers (Miguel Garcia-Perez [8]) already smashed the paper to pieces.

The authors’ method is inadequate and their conclusion is not logically connected to their results. No revision (major, minor, or discretionary) will save this work. (…)

Miguel’s well funded criticism was not well addressed by the authors [9]. Apparently the editors didn’t see through and relied on the second peer reviewer [10], who merely said it was a “great job” etcetera, but that recall should not be written with a capital R.
(and that was about the only revision the authors made)

Perhaps it needs another paper to convince Gehanno et al and the uncritical readers of their manuscript.

Such a paper might just have been published [11]. It is written by Dean Giustini and Maged Kamel Boulos and is entitled:

Google Scholar is not enough to be used alone for systematic reviews

It is a simple and straightforward paper, but it makes its points clearly.

Giustini and Kamel Boulos looked for a recent SR in their own area of expertise (Chou et al [12]), that included a comparable number of references as that of Gehanno et al. Next they test GS’ ability to locate these references.

Although most papers cited by Chou et al. (n=476/506;  ~95%) were ultimately found in GS, numerous iterative searches were required to find the references and each citation had to be managed once at a time. Thus GS was not able to locate all references found by Chou et al. and the whole exercise was rather cumbersome.

As expected, trying to find the papers by a “real-life” GS search was almost impossible. Because due to its rudimentary structure, GS did not understand the expert search strings and was unable to translate them. Thus using Chou et al.’s original search strategy and keywords yielded unmanageable results of approximately >750,000 items.

Giustini and Kamel Boulos note that GS’ ability to search into the full-text of papers combined with its PageRank’s algorithm can be useful.

On the other hand GS’ changing content, unknown updating practices and poor reliability make it an inappropriate sole choice for systematic reviewers:

As searchers, we were often uncertain that results found one day in GS had not changed a day later and trying to replicate searches with date delimiters in GS did not help. Papers found today in GS did not mean they would be there tomorrow.

But most importantly, not all known items could be found and the search process and selection are too cumbersome.

Thus shall we now for once and for all conclude that GS is NOT sufficient to be used alone for SR searching?

We don’t need another bad paper addressing this.

But I would really welcome a well performed paper looking at the additional value of a GS in SR-searching. For I am sure that GS may be valuable for some questions and some topics in some respects. We have to find out which.

References

  1. PubMed versus Google Scholar for Retrieving Evidence 2010/06 (laikaspoetnik.wordpress.com)
  2. Google scholar for systematic reviews…. hmmmm  2013/01 (ilk21.wordpress.com)
  3. Anders M.E. & Evans D.P. (2010) Comparison of PubMed and Google Scholar literature searches, Respiratory care, May;55(5):578-83  PMID:
  4. Gehanno J.F., Rollin L. & Darmoni S. (2013). Is the coverage of Google Scholar enough to be used alone for systematic reviews., BMC medical informatics and decision making, 13:7  PMID:  (open access)
  5. Is Google scholar enough for SR searching? No. 2013/01 (blogs.ubc.ca/dean)
  6. What’s Wrong With Google Scholar for “Systematic” Review 2013/01 (etechlib.wordpress.com)
  7. Comments at Gehanno’s paper (www.biomedcentral.com)
  8. Official Reviewer’s report of Gehanno’s paper [1]: Miguel Garcia-Perez, 2012/09
  9. Authors response to comments  (www.biomedcentral.com)
  10. Official Reviewer’s report of Gehanno’s paper [2]: Henrik von Wehrden, 2012/10
  11. Giustini D. & Kamel Boulos M.N. (2013). Google Scholar is not enough to be used alone for systematic reviews, Online Journal of Public Health Informatics, 5 (2) DOI:
  12. Chou W.Y.S., Prestin A., Lyons C. & Wen K.Y. (2013). Web 2.0 for Health Promotion: Reviewing the Current Evidence, American Journal of Public Health, 103 (1) e9-e18. DOI:




Things to Keep in Mind when Searching OVID MEDLINE instead of PubMed

25 11 2011

When I search extensively for systematic reviews I prefer OVID MEDLINE to PubMed for several reasons. Among them, it is easier to build a systematic search in OVID, the search history has a more structured format that is easy to edit, the search features are more advanced giving you more control over the search and translation of the a search to OVID EMBASE, PSYCHINFO and the Cochrane Library is “peanuts”, relatively speaking.

However, there are at least two things to keep in mind when searching OVID MEDLINE instead of PubMed.

1. You may miss publications, most notably recent papers.

PubMed doesn’t only provide access to MEDLINE, but also contains some other citations, including in-process citations which provide a record for an article before it is indexed with MeSH and added to MEDLINE.

As previously mentioned, I once missed a crucial RCT that was available in PubMed, but not yet available in OVID/MEDLINE.

A few weeks ago one of my clients said that she found 3 important papers with a simple PubMed search that were not retrieved by my exhaustive OVID MEDLINE (Doh!).
All articles were recent ones [Epub ahead of print, PubMed – as supplied by publisher]. I checked that these articles were indeed not yet included in OVID MEDLINE, and they weren’t.

As said, PubMed doesn’t have all search features of OVID MEDLINE and I felt a certain reluctance to make a completely new exhaustive search in PubMed. I would probably retrieve many irrelevant papers which I had tried to avoid by searching OVID*. I therefore decided to roughly translate the OVID search using textwords only (the missed articles had no MESH attached). It was a matter of copy-pasting the single textwords from the OVID MEDLINE search (and omitting adjacency operators) and adding the command [tiab], which means that terms are searched as textwords (in title and abstract) in PubMed (#2, only part of the long search string is shown).

To see whether all articles missed in OVID were in the non-MEDLINE set, I added the command: NOT MEDLINE[sb] (#3). Of the 332 records (#2), 28 belonged to the non-MEDLINE subset. All 3 relevant articles, not found in OVID MEDLINE, were in this set.

In total, there were 15 unique records not present in the OVID MEDLINE and EMBASE search. This additional search in PubMed was certainly worth the effort as it yielded more than 3 new relevant papers. (Apparently there was a boom in relevant papers on the topic, recently)

In conclusion, when doing an exhaustive search in OVID MEDLINE it is worth doing an additional search in PubMed to find the non-MEDLINE papers. Regularly these are very relevant papers that you wouldn’t like to have missed. Dependent on your aim you can suffice with a simpler, broader search for only textwords and limit by using NOT MEDLINE[sb].**

From now on, I will always include this PubMed step in my exhaustive searches. 

2. OVID MEDLINE contains duplicate records

I use Reference Manager to deduplicate the records retrieved from all databases  and I share the final database with my client. I keep track of the number of hits in each database and of the number of duplicates to facilitate the reporting of the search procedure later on (using the PRISM flowchart, see above). During this procedure, I noticed that I always got LESS records in Reference Manager when I imported records from OVID MEDLINE, but not when I imported records from the other databases. Thus it appears that OVID MEDLINE contains duplicate records.

For me it was just a fact that there were duplicate records in OVID MEDLINE. But others were surprised to hear this.

Where everyone just wrote down the number of total number hits in OVID MEDLINE, I always used the number of hits after deduplication in Reference Manager. But this is a quite a detour and not easy to explain in the PRISM-flowchart.

I wondered whether this deduplication could be done in OVID MEDLINE directly. I knew you cold deduplicate a multifile search, but would it also be possible to deduplicate a set from one database only? According to OVID help there should be a button somewhere, but I couldn’t find it (curious if you can).

Googling I found another OVID manual saying :

..dedup n = Removes duplicate records from multifile search results. For example, ..dedup 5 removes duplicate records from the multifile results set numbered 5.

Although the manual only talked about “multifile searches”, I tried the comment (..dedup 34) on the final search set (34) in OVID MEDLINE, and voilà, 21 duplicates were found (exactly the same number as removed by Reference manager)

The duplicates had the same PubMed ID (PMID, the .an. command in OVID), and were identical or almost identical.

Differences that I noticed were minimal changes in the MeSH (i.e. one or more MeSH  and/or subheadings changed) and changes in journal format (abbreviation used instead of full title).

Why are these duplicates present in OVID MEDLINE and not in PubMed?

These are the details of the PMID 20846254 in OVID (2 records) and in PubMed (1 record)

The Electronic Date of Publication (PHST)  was September 16th 2010. 2 days later the record was included in PubMed , but MeSH were added 3 months later ((MHDA: 2011/02/12). Around this date records are also entered in OVID MEDLINE. The only difference between the 2 records in OVID MEDLINE is that one record appears to be revised at 2011-10-13, whereas the other is not.

The duplicate records of 18231698 have again the same creation date (20080527) and entry date (20081203), but one is revised 2110-20-09 and updated 2010-12-14, while the other is revised 2011-08-18 and updated 2011-08-19 (thus almost one year later).

Possibly PubMed changes some records, instantaneously replacing the old ones, but OVID only includes the new PubMed records during MEDLINE-updates and doesn’t delete the old version.

Anyway, wouldn’t it be a good thing if OVID deduplicated its MEDLINE records on a daily basis or would replace the old ones when loading  new records from MEDLINE?

In the meantime, I would recommend to apply the deduplicate command yourself to get the exact number of unique records retrieved by your search in OVID MEDLINE.

*mostly because PubMed doesn’t have an adjacency-operator.
** Of course, only if you have already an extensive OVID MEDLINE search.





PubMed’s Higher Sensitivity than OVID MEDLINE… & other Published Clichés.

21 08 2011

ResearchBlogging.orgIs it just me, or are biomedical papers about searching for a systematic review often of low quality or just too damn obvious? I’m seldom excited about papers dealing with optimal search strategies or peculiarities of PubMed, even though it is my specialty.
It is my impression, that many of the lower quality and/or less relevant papers are written by clinicians/researchers instead of information specialists (or at least no medical librarian as the first author).

I can’t help thinking that many of those authors just happen to see an odd feature in PubMed or encounter an unexpected  phenomenon in the process of searching for a systematic review.
They think: “Hey, that’s interesting” or “that’s odd. Lets write a paper about it.” An easy way to boost our scientific output!
What they don’t realize is that the published findings are often common knowledge to the experienced MEDLINE searchers.

Lets give two recent examples of what I think are redundant papers.

The first example is a letter under the heading “Clinical Observation” in Annals of Internal Medicine, entitled:

“Limitations of the MEDLINE Database in Constructing Meta-analyses”.[1]

As the authors rightly state “a thorough literature search is of utmost importance in constructing a meta-analysis. Since the PubMed interface from the National Library of Medicine is a cornerstone of many meta-analysis,  the authors (two MD’s) focused on the freely available PubMed” (with MEDLINE as its largest part).

The objective was:

“To assess the accuracy of MEDLINE’s “human” and “clinical trial” search limits, which are used by authors to focus literature searches on relevant articles.” (emphasis mine)

O.k…. Stop! I know enough. This paper should have be titled: “Limitation of Limits in MEDLINE”.

Limits are NOT DONE, when searching for a systematic review. For the simple reason that most limits (except language and dates) are MESH-terms.
It takes a while before the indexers have assigned a MESH to the papers and not all papers are correctly (or consistently) indexed. Thus, by using limits you will automatically miss recent, not yet, or not correctly indexed papers. Whereas it is your goal (or it should be) to find as many relevant papers as possible for your systematic review. And wouldn’t it be sad if you missed that one important RCT that was published just the other day?

On the other hand, one doesn’t want to drown in irrelevant papers. How can one reduce “noise” while minimizing the risk of loosing relevant papers?

  1. Use both MESH and textwords to “limit” you search, i.e. also search “trial” as textword, i.e. in title and abstract: trial[tiab]
  2. Use more synonyms and truncation (random*[tiab] OR  placebo[tiab])
  3. Don’t actively limit but use double negation. Thus to get rid of animal studies, don’t limit to humans (this is the same as combining with MeSH [mh]) but safely exclude animals as follows: NOT animals[mh] NOT humans[mh] (= exclude papers indexed with “animals” except when these papers are also indexed with “humans”).
  4. Use existing Methodological Filters (ready-made search strategies) designed to help focusing on study types. These filters are based on one or more of the above-mentioned principles (see earlier posts here and here).
    Simple Methodological Filters can be found at the PubMed Clinical Queries. For instance the narrow filter for Therapy not only searches for the Publication Type “Randomized controlled trial” (a limit), but also for randomized, controlled ànd  trial  as textwords.
    Usually broader (more sensitive) filters are used for systematic reviews. The Cochrane handbook proposes to use the following filter maximizing precision and sensitivity to identify randomized trials in PubMed (see http://www.cochrane-handbook.org/):
    (randomized controlled trial [pt] OR controlled clinical trial [pt] OR randomized [tiab] OR placebo [tiab] OR clinical trials as topic [mesh: noexp] OR randomly [tiab] OR trial [ti]) NOT (animals [mh] NOT humans [mh]).
    When few hits are obtained, one can either use a broader filter or no filter at all.

In other words, it is a beginner’s mistake to use limits when searching for a systematic review.
Besides that the authors publish what should be common knowledge (even our medical students learn it) they make many other (little) mistakes, their precise search is difficult to reproduce and far from complete. This is already addressed by Dutch colleagues in a comment [2].

The second paper is:

PubMed had a higher sensitivity than Ovid-MEDLINE in the search for systematic reviews [3], by Katchamart et al.

Again this paper focuses on the usefulness of PubMed to identify RCT’s for a systematic review, but it concentrates on the differences between PubMed and OVID in this respect. The paper starts with  explaining that PubMed:

provides access to bibliographic information in addition to MEDLINE, such as in-process citations (..), some OLDMEDLINE citations (….) citations that precede the date that a journal was selected for MEDLINE indexing, and some additional life science journals that submit full texts to PubMed Central and receive a qualitative review by NLM.

Given these “facts”, am I exaggerating when I am saying that the authors are pushing at an open door when their main conclusion is that PubMed retrieved more citations overall than Ovid-MEDLINE? The one (!) relevant article missed in OVID was a 2005 study published in a Japanese journal that MEDLINE started indexing in 2007. It was therefore in PubMed, but not in OVID MEDLINE.

An important aspect to keep in mind when searching OVID/MEDLINE ( I have earlier discussed here and here). But worth a paper?

Recently, after finishing an exhaustive search in OVID/MEDLINE, we noticed that we missed a RCT in PubMed, that was not yet available in OVID/MEDLINE.  I just added one sentence to the search methods:

Additionally, PubMed was searched for randomized controlled trials ahead of print, not yet included in OVID MEDLINE. 

Of course, I could have devoted a separate article to this finding. But it is so self-evident, that I don’t think it would be worth it.

The authors have expressed their findings in sensitivity (85% for Ovid-MEDLINE vs. 90% for PubMed, 5% is that ONE paper missing), precision and  number to read (comparable for OVID-MEDLINE and PubMed).

If I might venture another opinion: it looks like editors of medical and epidemiology journals quickly fall for “diagnostic parameters” on a topic that they don’t understand very well: library science.

The sensitivity/precision data found have little general value, because:

  • it concerns a single search on a single topic
  • there are few relevant papers (17- 18)
  • useful features of OVID MEDLINE that are not available in PubMed are not used. I.e. Adjacency searching could enhance the retrieval of relevant papers in OVID MEDLINE (adjacency=words searched within a specified maximal distance of each other)
  • the searches are not comparable, nor are the search field commands.

The latter is very important, if one doesn’t wish to compare apples and oranges.

Lets take a look at the first part of the search (which is in itself well structured and covers many synonyms).
First part of the search - Click to enlarge
This part of the search deals with the P: patients with rheumatoid arthritis (RA). The authors first search for relevant MeSH (set 1-5) and then for a few textwords. The MeSH are fine. The authors have chosen to use Arthritis, rheumatoid and a few narrower terms (MeSH-tree shown at the right). The authors have taken care to use the MeSH:noexp command in PubMed to prevent the automatic explosion of narrower terms in PubMed (although this is superfluous for MesH terms having no narrow terms, like Caplan syndrome etc.).

But the fields chosen for the free text search (sets 6-9) are not comparable at all.

In OVID the mp. field is used, whereas all fields or even no fields are used in PubMed.

I am not even fond of the uncontrolled use of .mp (I rather search in title and abstract, remember we already have the proper MESH-terms), but all fields is even broader than .mp.

In general a .mp. search looks in the Title, Original Title, Abstract, Subject Heading, Name of Substance, and Registry Word fields. All fields would be .af in OVID not .mp.

Searching for rheumatism in OVID using the .mp field yields 7879 hits against 31390 hits when one searches in the .af field.

Thus 4 times as much. Extra fields searched are for instance the journal and the address field. One finds all articles in the journal Arthritis & Rheumatism for instance [line 6], or papers co-authored by someone of the dept. of rheumatoid surgery [line 9]

Worse, in PubMed the “all fields” command doesn’t prevent the automatic mapping.

In PubMed, Rheumatism[All Fields] is translated as follows:

“rheumatic diseases”[MeSH Terms] OR (“rheumatic”[All Fields] AND “diseases”[All Fields]) OR “rheumatic diseases”[All Fields] OR “rheumatism”[All Fields]

Oops, Rheumatism[All Fields] is searched as the (exploded!) MeSH rheumatic diseases. Thus rheumatic diseases (not included in the MeSH-search) plus all its narrower terms! This makes the entire first part of the PubMed search obsolete (where the authors searched for non-exploded specific terms). It explains the large difference in hits with rheumatism between PubMed and OVID/MEDLINE: 11910 vs 6945.

Not only do the authors use this .mp and [all fields] command instead of the preferred [tiab] field, they also apply this broader field to the existing (optimized) Cochrane filter, that uses [tiab]. Finally they use limits!

Well anyway, I hope that I made my point that useful comparison between strategies can only be made if optimal strategies and comparable  strategies are used. Sensitivity doesn’t mean anything here.

Coming back to my original point. I do think that some conclusions of these papers are “good to know”. As a matter of fact it should be basic knowledge for those planning an exhaustive search for a systematic review. We do not need bad studies to show this.

Perhaps an expert paper (or a series) on this topic, understandable for clinicians, would be of more value.

Or the recognition that such search papers should be designed and written by librarians with ample experience in searching for systematic reviews.

NOTE:
* = truncation=search for different word endings; [tiab] = title and abstract; [ti]=title; mh=mesh; pt=publication type

Photo credit

The image is taken from the Dragonfly-blog; here the Flickr-image Brain Vocab Sketch by labguest was adapted by adding the Pubmed logo.

References

  1. Winchester DE, & Bavry AA (2010). Limitations of the MEDLINE database in constructing meta-analyses. Annals of internal medicine, 153 (5), 347-8 PMID: 20820050
  2. Leclercq E, Kramer B, & Schats W (2011). Limitations of the MEDLINE database in constructing meta-analyses. Annals of internal medicine, 154 (5) PMID: 21357916
  3. Katchamart W, Faulkner A, Feldman B, Tomlinson G, & Bombardier C (2011). PubMed had a higher sensitivity than Ovid-MEDLINE in the search for systematic reviews. Journal of clinical epidemiology, 64 (7), 805-7 PMID: 20926257
  4. Search OVID EMBASE and Get MEDLINE for Free…. without knowing it (laikaspoetnik.wordpress.com 2010/10/19/)
  5. 10 + 1 PubMed Tips for Residents (and their Instructors) (laikaspoetnik.wordpress.com 2009/06/30)
  6. Adding Methodological filters to myncbi (laikaspoetnik.wordpress.com 2009/11/26/)
  7. Search filters 1. An Introduction (laikaspoetnik.wordpress.com 2009/01/22/)




A Filter for Finding “All Studies on Animal Experimentation in PubMed”

29 09 2010

ResearchBlogging.orgFor  an introduction to search filters you can first read this post.

Most people searching PubMed try to get rid of publications about animals. But basic scientists and lab animal technicians just want to find those animal studies.

PubMed has built-in filters for that: the limits. There is a limit  for “humans” and a limit for “animals”. But that is not good enough to find each and every article about humans, respectively animals. The limits are MeSH, Medical Subject Headings or index-terms and these are per definition not added to new articles, that haven’t been indexed yet. To name the main disadvantage…
Thus to find all papers one should at least search for other relevant MeSH and textwords (words in title and abstract) too.

A recent paper published in Laboratory Animals describes a filter for finding “all studies on animal experimentation in PubMed“, to facilitate “writing a systematic review (SR) of animal research” .

As the authors rightly emphasize, SR’s are no common practice in the field of animal research. Knowing what already has been done can prevent unnecessary duplication of animal experiments and thus unnecessary animal use. The authors have interesting ideas like registration of animal studies (similar to clinical trials registers).

In this article they describe the design of an animal filter for PubMed. The authors describe their filter as follows:

“By using our effective search filter in PubMed, all available literature concerning a specific topic can be found and read, which will help in making better evidencebased decisions and result in optimal experimental conditions for both science and animal welfare.”

Is this conclusion justified?

Design of the filter

Their filter is subjectively derived: the terms are “logically” chosen.

[1] The first part of the animal filter consists of only MeSH-terms.

You can’t use animals[mh] (mh=Mesh) as a search term, because MeSH are automatically exploded in PubMed. This means that narrower terms (lower in the tree) are also searched. If “Animals” were allowed to explode, the search would include the MeSH, “Humans”, which is at the end of one tree (primates etc, see Fig. below)

Therefore the MeSH-parts of their search consists of:

  1. animals [mh:noexp]: only articles are found that are indexed with “animals”, but not its narrower terms. Notably, this is identical to the PubMed Limit: animals).
  2. Exploded Animal-specific MeSH-terms not having humans as a narrow term, i.e. “fishes”[MeSH Terms].
  3. and non-exploded MeSH in those cases that humans occurred in the same branch. Like “primates”[MeSH Terms:noexp]
  4. In addition two other MeSH are used: “animal experimentation”[MeSH Terms] and “models, animal”[MeSH Terms]

[2] The second part of the search filter consist of terms in the title and abstract (command: [tiab]).

The terms are taken from relevant MeSH, two reports about animal experimentation in the Netherlands and in Europe, and the experience of the authors, who are experts in the field.

The authors use this string for non-indexed records (command: NOT medline[sb]). Thus this part is only meant to find records that haven’t (yet) been indexed, but in which (specific) animals are mentioned by the author in title or text. Synonyms and spelling variants have been taken into account.

Apparently the authors have chosen NOT to search for text words in indexed records only. Presumably it gives too much noise, to search for animals mentioned in non-indexed articles. However, the authors do not discuss why this was necessary.

This search string is extremely long. Partly because truncation isn’t used with the longer words: i.e. nematod*[tiab] instead of nematoda[Tiab] OR nematode[Tiab] OR nematoda[Tiab] OR nematode[Tiab] OR nematodes[Tiab]. Partly because they aim for completeness. However the usefulness of the terms as such hasn’t been verified (see below).

Search strategies can be freely accessed here.

Validation

The filter is mainly validated against the PubMed Limit “Animals”.

The authors assume that the PubMed Limits are “the most easily available and most obvious method”. However I know few librarians or authors of systematic reviews who would solely apply this so called ‘regular method’. In the past I have used exactly the same MeSH-terms (1) and the main text words (2) as included in their filter.

Considering that the filter includes the PubMed limit “Animals” [1.1] it does not come as a surprise that the sensitivity of the filter exceeds that of the PubMed limit Animals…

Still, the sensitivity (106%) is not really dramatic: 6% more records are found, the PubMed Limit “animals” is set as 100%.

Apparently records are very well indexed with the MeSH “animals”. Few true animal records are missed, because “animals” is a check tag. A check tag is a MeSH that is looked for routinely by indexers in every journal article. It is added to the record even if it isn’t the main (or major) point of an article.

Is an increased sensitivity of appr. 6% sufficient to conclude that this filter “performs much better than the current alternative in PubMed”?

No. It is not only important that MORE is found but to what degree the extra hits are relevant. Surprisingly, the authors ONLY determined SENSITIVITY, not specificity or precision.

There are many irrelevant hits, partly caused by the inclusion of animal population groups[mesh], which has some narrower terms that often not used for experimentation, i.e. endangered species.

Thus even after omission of animal population groups[mesh], the filter still gives hits like:

These are evidently NOT laboratory animal experiments and mainly caused by the inclusion invertebrates  like plankton.

Most other MeSH are not extremely useful either. Even terms as animal experimentation[mh] and models, animal[mh] are seldom assigned to experimental studies lacking animals as a MeSH.

According to the authors, the MeSH “Animals” will not retrieve studies solely indexed with the MeSH term Mice. However, the first records missed with mice[mesh] NOT animals[mh:noexp] are from 1965, when they apparently didn’t use “animals” as a check tag in addition to specific ‘animal’ MeSH.

Thus presumably the MeSH-filter can be much shorter and need only contain animal MeSH (rats[mh], mice[mh] etc) when publications older than 1965 are also required.

The types of vertebrate animals used in lab re...

Image via Wikipedia

Their text word string (2) is also extremely long.  Apart from the lack of truncation, most animal terms are not relevant for most searches. 2/3 of the experiments are done with rodents (see Fig). The other animals are often used for specific experiments (zebra-fish, Drosophila) or in another context, not related to animal experiments, such as:

swine flu, avian flu, milk production by cows, or allergy by milk-products or mites, stings by insects and bites by dogs and of course fish, birds, cattle and poultry as food, fetal calf serum in culture medium, but also vaccination with “mouse products” in humans. Thus most of the terms produce noise for most topics. An example below (found by birds[mesh] :-)

On the other hand strains of mice and rats are missing from the search string: i.e. balb/c, wistar.

Extremely long search strings (1 page) are also annoying to use. However, the main issue is whether the extra noise matters. Because the filter is meant to find all experimental animal studies.

As Carlijn Hooijmans notices correctly, the filters are never used on their own, only in combination with topic search terms.

Hooijmans et al have therefore “validated” their filter with two searches. “Validated” between quotation marks because they have only compared the number of hits, thus the increase in sensitivity.

Their first topic is the use of probiotics in experimental pancreatitis (see appendix).

Their filter (combined with the topic search) retrieved 37 items against 33 items with the so called “regular method”: an increase in sensitivity of 21,1%.

After updating the search I got  38 vs 35 hits. Two of the 3 extra hits obtained with the broad filter are relevant and are missed with the PubMed limit for animals, because the records haven’t been indexed. They could also have been found with the text words pig*[tiab] or dog*[tiab]. Thus the filter is ok for this purpose, but unnecessary long. The MeSH-part of the filter had NO added value compared to animals[mh:noexp].

Since there are only 148 hits without the use of any filters, researchers could also use screen all hits. Alternatively there is a trick to safely exclude human studies:

NOT (humans[mh] NOT animals[mh:noexp])

With this double negation you exclude PubMed records that are indexed with humans[mh], as long as these records aren’t indexed with animals[mh:noexp] too. It is far “safer” than limiting for “animals”[mesh:noexp] only. We use a similar approach to ” exclude”  animals when we search for human studies.

This extremely simple filter yields 48 hits, finding all hits found with the large animal filter (plus 10 irrelevant hits).

Such a simple filter can easily be used for searches with relatively few hits, but gives too many irrelevant hits in case of  a high yield.

The second topic is food restriction. 9280 Records were obtained with the Limit: “Animals”, whereas this strategy combined with the complete filter retrieved 9650 items. The sensitivity in this search strategy was therefore 104%. 4% extra hits were obtained.

The MeSH-search added little to the search. Only 21 extra hits. The relevant hits were (again) only from before 1965.

The text-word part of the search finds relevant new articles, although there are quite some  irrelevant findings too, i.e. dieting and obtaining proteins from chicken.

4% isn’t a lot extra, but the aim of the researchers is too find all there is.

However, it is the question whether researchers want to find every single experiment or observation done in the animal kingdom. If I were to plan an experiment on whether food restriction lowers the risk for prostate cancer in a transgenic mice, need I know what the effects are of food restriction on Drosophila, nematodes, salmon or even chicken on whatever outcome? Would I like to screen 10,000 hits?

Probably most researchers would like separate filters for rodents and other laboratory animals (primates, dogs) and for work on Drosophila or fish. In some fields there might also be a need to filter clinical trials and reviews out.

Furthermore, it is not only important to have a good filter but also a good search.

The topic searches in the current paper are not ideal: they contain overlapping terms (food restriction is also found by food and restriction) and misses important MeSH (Food deprivation, fasting and the broader term of caloric restriction “energy intake” are assigned more often to records about food deprivation than caloric restriction).

Their search:

(“food restriction”[tiab] OR (“food”[tiab] AND “restriction”[tiab]) OR “feed restriction”[tiab] OR (“feed”[tiab] AND “restriction”[tiab]) OR “restricted feeding”[tiab] OR (“feeding”[tiab] AND “restricted”[tiab]) OR “energy restriction”[tiab] OR (“energy”[tiab] AND “restriction”[tiab]) OR “dietary restriction”[tiab] OR (dietary”[tiab] AND “restriction”[tiab]) OR “caloric restriction”[MeSH Terms] OR (“caloric”[tiab] AND “restriction”[tiab]) OR “caloric restriction”[tiab])
might for instance be changed to:

Energy Intake[mh] OR Food deprivation[mh] OR Fasting[mh] OR food restrict*[tiab] OR feed restrict*[tiab] OR restricted feed*[tiab] OR energy restrict*[tiab] OR dietary restrict*[tiab] OR  caloric restrict*[tiab] OR calorie restrict*[tiab] OR diet restrict*[tiab]

You do not expect such incomplete strategies from people who repeatedly stress that: most scientists do not know how to use PubMed effectively” and that “many researchers do not use ‘Medical Subject Headings’ (MeSH terms), even though they work with PubMed every day”…..

Combining this modified search with their animal filter yields 21920 hits instead of 10335 as found with their “food deprivation” search and their animal filter. A sensitivity of 212%!!! Now we are talking! ;) (And yes there are many new relevant hits found)

Summary

The paper describes the performance of a subjective search filter to find all experimental studies performed with laboratory animals. The authors have merely “validated”  this filter against the Pubmed Limits: animals. In addition, they only determined sensitivity:  on average 7% more hits were obtained with the new animal filter than with the PubMed limit alone.

The authors have not determined the specificity or precision of the filter, not even for the 2 topics where they have applied the filter. A quick look at the results shows that the MeSH-terms other than the PubMed limit “animals” contributed little to the enhanced sensitivity. The text word part of the filter yields more relevant hits. Still -depending on the topic- there are many irrelevant records found, because  it is difficult to separate animals as food, allergens etc from laboratory animals used in experiments and the filter is developed to find every single animal in the animal kingdom, including poultry, fish, nematodes, flies, endangered species and plankton. Another (hardly to avoid) “contamination” comes from in vitro experiments with animal cells, animal products used in clinical trials and narrative reviews.

In practice, only parts of the search filter seem useful for most systematic reviews, and especially if these reviews are not meant to give an overview of all findings in the universe, but are needed to check if a similar experiment hasn’t already be done. It seems impractical if researchers have to make a systematic review, checking, summarizing and appraising  10,000 records each time they start a new experiment.

Perhaps I’m somewhat too critical, but the cheering and triumphant tone of the paper in combination with a too simple design and without proper testing of the filter asked for a critical response.

Credits

Thanks to Gerben ter Riet for alerting me to the paper. He also gave the tip that the paper can be obtained here for free.

References

  1. Hooijmans CR, Tillema A, Leenaars M, & Ritskes-Hoitinga M (2010). Enhancing search efficiency by means of a search filter for finding all studies on animal experimentation in PubMed. Laboratory animals, 44 (3), 170-5 PMID: 20551243

———————-





Will Nano-Publications & Triplets Replace The Classic Journal Articles?

23 06 2010

ResearchBlogging.org“Libraries and journals articles as we know them will cease to exists” said Barend Mons at the symposium in honor of our Library 25th Anniversary (June 3rd). “Possibly we will have another kind of party in another 25 years”…. he continued, grinning.

What he had to say the next half hour intrigued me. And although I had no pen with me (it was our party, remember), I thought it was interesting enough to devote a post to it.

I’m basing this post not only on my memory (we had a lot of Italian wine at the buffet), but on an article Mons referred to [1], a Dutch newspaper article [2]), other articles [3-6] and Powerpoints [7-9] on the topic.

This is a field I know little about, so I will try to keep it simple (also for my sake).

Mons started by touching on a problem that is very familiar to doctors, scientists and librarians: information overload by a growing web of linked data.  He showed a picture that looked like the one at the right (though I’m sure those are Twitter Networks).

As he said elsewhere [3]:

(..) the feeling that we are drowning in information is widespread (..) we often feel that we have no satisfactory mechanisms in place to make sense of the data generated at such a daunting speed. Some pharmaceutical companies are apparently seriously considering refraining from performing any further genome-wide association studies (… whole genome association –…) as the world is likely to produce many more data than these companies will ever be able to analyze with currently available methods .

With the current search engines we have to do a lot of digging to get the answers [8]. Computers are central to this digging, because there is no way people can stay updated, even in their own field.

However,  computers can’t deal with the current web and the scientific  information as produced in the classic articles (even the electronic versions), because of the following reasons:

  1. Homonyms. Words that sound or are the same but have a different meaning. Acronyms are notorious in this respect. Barend gave PSA as an example, but, without realizing it, he used a better example: PPI. This means Protein Pump Inhibitor to me, but apparently Protein Protein Interactions to him.
  2. Redundancy. To keep journal articles readable we often use different words to denote the same. These do not add to the real new findings in a paper. In fact the majority of digital information is duplicated repeatedly. For example “Mosquitoes transfer malaria”, is a factual statement repeated in many consecutive papers on the subject.
  3. The connection between words is not immediately clear (for a computer). For instance, anti-TNF inhibitors can be used to treat skin disorders, but the same drugs can also cause it.
  4. Data are not structured beforehand.
  5. Weight: some “facts” are “harder” than others.
  6. Not all data are available or accessible. Many data are either not published (e.g. negative studies), not freely available or not easy to find.  Some portals (GoPubmed, NCBI) provide structural information (fields, including keywords), but do not enable searching full text.
  7. Data are spread. Data are kept in “data silos” not meant for sharing [8](ppt2). One would like to simultaneously query 1000 databases, but this would require semantic web standards for publishing, sharing and querying knowledge from diverse sources…..

In a nutshell, the problem is as Barend put it: “Why bury data first and then mine it again?” [9]

Homonyms, redundancy and connection can be tackled, at least in the field Barend is working in (bioinformatics).

Different terms denoting the same concept (i.e. synonyms) can be mapped to a single concept identifier (i.e. a list of synonyms), whereas identical terms used to indicate different concepts (i.e. homonyms) can be resolved by a disambiguation algorithm.

The shortest meaningful sentence is a triplet: a combination of subject, predicate and object. A triplet indicates the connection and direction.  “Mosquitoes cause/transfer malaria”  is such a triplet, where mosquitoes and malaria are concepts. In the field of proteins: “UNIPROT 05067 is a protein” is a triplet (where UNIPROT 05067 and protein are concepts), as are: “UNIprotein 05067 is located in the membrane” and “UNIprotein 0506 interacts with UNIprotein 0506″[8].  Since these triplets  (statements)  derive from different databases, consistent naming and availability of  information is crucial to find them. Barend and colleagues are the people behind Wikiproteins, an open, collaborative wiki  focusing on proteins and their role in biology and medicine [4-6].

Concepts and triplets are widely accepted in the world of bio-informatics. To have an idea what this means for searching, see the search engine Quertle, which allows semantic search of PubMed & full-text biomedical literature, automatic extraction of key concepts; Searching for ESR1 $BiologicalProcess will search abstracts mentioning all kind of processes where ESR1 (aka ERα, ERalpha, EStrogen Receptor 1) are involved. The search can be refined by choosing ‘narrower terms’ like “proliferation” or “transcription”.

The new aspects is that Mons wants to turn those triplets into (what he calls) nano-publications. Because not every statement is as ‘hard’, nano-publications are weighted by assigning numbers from 0 (uncertain) to 1 (very certain). The nano-publication “mosquitoes transfer malaria” will get a number approaching 1.

Such nano-publications offer little shading and possibility for interpretation and discussion. Mons does not propose to entirely replace traditional articles by nano-publications. Quote [3]:

While arguing that research results should be available in the form of nano-publications, are emphatically not saying that traditional, classical papers should not be published any longer. But their role is now chiefly for the official record, the “minutes of science” , and not so much as the principle medium for the exchange of scientific results. That exchange, which increasingly needs the assistance of computers to be done properly and comprehensively, is best done with machine-readable, semantically consistent nano-publications.

According to Mons, authors and their funders should start requesting and expecting the papers that they have written and funded to be semantically coded when published, preferably by the publisher and otherwise by libraries: the technology exists to provide Web browsers with the functionality for users to identify nano-publications, and annotate them.

Like the wikiprotein-wiki, nano-publications will be entirely open access. It will suffice to properly cite the original finding/publication.

In addition there is a new kind of “peer review”. An expert network is set up to immediately assess a twittered nano-publication when it comes out, so that  the publication is assessed by perhaps 1000 experts instead of 2 or 3 reviewers.

On a small-scale, this is already happening. Nano-publications are send as tweets to people like Gert Jan van Ommen (past president of HUGO and co-author of 5 of my publications (or v.v.)) who then gives a red (don’t believe) or a green light (believe) via one click on his blackberry.

As  Mons put it, it looks like a subjective event, quite similar to “dislike” and “like” in social media platforms like Facebook.

Barend often referred to a PLOS ONE paper by van Haagen et al [1], showing the superiority of the concept-profile based approach not only in detecting explicitly described PPI’s, but also in inferring new PPI’s.

[You can skip the part below if you’re not interested in details of this paper]

Van Haagen et al first established a set of a set of 61,807 known human PPIs and of many more probable Non-Interacting Protein Pairs (NIPPs) from online human-curated databases (and NIPPs also from the IntAct database).

For the concept-based approach they used the concept-recognition software Peregrine, which includes synonyms and spelling variations  of concepts and uses simple heuristics to resolve homonyms.

This concept-profile based approach was compared with several other approaches, all depending on co-occurrence (of words or concepts):

  • Word-based direct relation. This approach uses direct PubMed queries (words) to detect if proteins co-occur in the same abstract (thus the names of two proteins are combined with the boolean ‘AND’). This is the simplest approach and represents how biologists might use PubMed to search for information.
  • Concept-based direct relation (CDR). This approach uses concept-recognition software to find PPIs, taking synonyms into account, and resolving homonyms. Here two concepts (h.l. two proteins) are detected if they co-occur in the same abstract.
  • STRING. The STRING database contains a text mining score which is based on direct co-occurrences in literature.

The results show that, using concept profiles, 43% of the known PPIs were detected, with a specificity of 99%, and 66% of all known PPIs with a specificity of 95%. In contrast, the direct relations methods and STRING show much lower scores:

Word-based CDR Concept profiles STRING
Sensitivity at spec = 99% 28% 37% 43% 39%
Sensitivity at spec = 95% 33% 41% 66% 41%
Area under Curve 0.62 0.69 0.90 0.69

These findings suggested that not all proteins with high similarity scores are known to interact but may be related in another way, e.g.they could be involved in the same pathway or be part of the same protein complex, but do not physically interact. Indeed concept-based profiling was superior in predicting relationships between proteins potentially present in the same complex or pathway (thus A-C inferred from concurrence protein pairs A-B and B-C).

Since there is often a substantial time lag between the first publication of a finding, and the time the PPI is entered in a database, a retrospective study was performed to examine how many of the PPIs that would have been predicted by the different methods in 2005 were confirmed in 2007. Indeed, using concept profiles, PPIs could be efficiently predicted before they enter PPI databases and before their interaction was explicitly described in the literature.

The practical value of the method for discovery of novel PPIs is illustrated by the experimental confirmation of the inferred physical interaction between CAPN3 and PARVB, which was based on frequent co-occurrence of both proteins with concepts like Z-disc, dysferlin, and alpha-actinin. The relationships between proteins predicted are broader than PPIs, and include proteins in the same complex or pathway. Dependent on the type of relationships deemed useful, the precision of the method can be as high as 90%.

In line with their open access policy, they have made the full set of predicted interactions available in a downloadable matrix and through the webtool Nermal, which lists the most likely interaction partners for a given protein.

According to Mons, this framework will be a very rich source for new discoveries, as it will enable scientists to prioritize potential interaction partners for further testing.

Barend Mons started with the statement that nano-publications will replace the classic articles (and the need for libraries). However, things are never as black as they seem.
Mons showed that a nano-publication is basically a “peer-reviewed, openly available” triplet. Triplets can be effectively retrieved ànd inferred from available databases/papers using a
concept-based approach.
Nevertheless, effectivity needs to be enhanced by semantically coding triplets when published.

What will this mean for clinical medicine? Bioinformatics is quite another discipline, with better structured and more straightforward data (interaction, identity, place). Interestingly, Mons and van Haage plan to do further studies, in which they will evaluate whether the use of concept profiles can also be applied in the prediction of other types of relations, for instance between drugs or genes and diseases. The future will tell whether the above-mentioned approach is also useful in clinical medicine.

Implementation of the following (implicit) recommendations would be advisable, independent of the possible success of nano-publications:

  • Less emphasis on “publish or perish” (thus more on the data themselves, whether positive, negative, trendy or not)
  • Better structured data, partly by structuring articles. This has already improved over the years by introducing structured abstracts, availability of extra material (appendices, data) online and by guidelines, such as STARD (The Standards for Reporting of Diagnostic Accuracy)
  • Open Access
  • Availability of full text
  • Availability of raw data

One might argue that disclosing data is unlikely when pharma is involved. It is very hopeful therefore, that a group of major pharmaceutical companies have announced that they will share pooled data from failed clinical trials in an attempt to figure out what is going wrong in the studies and what can be done to improve drug development (10).

Unfortunately I don’t dispose of Mons presentation. Therefore two other presentations about triplets, concepts and the semantic web.

&

References

  1. van Haagen HH, ‘t Hoen PA, Botelho Bovo A, de Morrée A, van Mulligen EM, Chichester C, Kors JA, den Dunnen JT, van Ommen GJ, van der Maarel SM, Kern VM, Mons B, & Schuemie MJ (2009). Novel protein-protein interactions inferred from literature context. PloS one, 4 (11) PMID: 19924298
  2. Twitteren voor de wetenschap, Maartje Bakker, Volskrant (2010-06-05) (Twittering for Science)
  3. Barend Mons and Jan Velterop (?) Nano-Publication in the e-science era (Concept Web Alliance, Netherlands BioInformatics Centre, Leiden University Medical Center.) http://www.nbic.nl/uploads/media/Nano-Publication_BarendMons-JanVelterop.pdf, assessed June 20th, 2010.
  4. Mons, B., Ashburner, M., Chichester, C., van Mulligen, E., Weeber, M., den Dunnen, J., van Ommen, G., Musen, M., Cockerill, M., Hermjakob, H., Mons, A., Packer, A., Pacheco, R., Lewis, S., Berkeley, A., Melton, W., Barris, N., Wales, J., Meijssen, G., Moeller, E., Roes, P., Borner, K., & Bairoch, A. (2008). Calling on a million minds for community annotation in WikiProteins Genome Biology, 9 (5) DOI: 10.1186/gb-2008-9-5-r89
  5. Science Daily (2008/05/08) Large-Scale Community Protein Annotation — WikiProteins
  6. Boing Boing: (2008/05/28) WikiProteins: a collaborative space for biologists to annotate proteins
  7. (ppt1) SWAT4LS 2009Semantic Web Applications and Tools for Life Sciences http://www.swat4ls.org/
    Amsterdam, Science Park, Friday, 20th of November 2009
  8. (ppt2) Michel Dumontier: triples for the people scientists liberating biological knowledge with the semantic web
  9. (ppt3, only slide shown): Bibliography 2.0: A citeulike case study from the Wellcome Trust Genome Campus – by Duncan Hill (EMBL-EBI)
  10. WSJ (2010/06/11) Drug Makers Will Share Data From Failed Alzheimer’s Trials




An Evidence Pyramid that Facilitates the Finding of Evidence

20 03 2010

Earlier I described that there are so many search- and EBM-pyramids that it is confusing. I described  3 categories of pyramids:

  1. Search Pyramids
  2. Pyramids of EBM-sources
  3. Pyramids of EBM-levels (levels of evidence)

In my courses where I train doctors and medical students how to find evidence quickly, I use a pyramid that is a mixture of 1. and 2. This is a slide from a 2007 course.

This pyramid consists of 4 layers (from top down):

  1. EBM-(evidence based) guidelines.
  2. Synopses & Syntheses*: a synopsis is a summary and critical appraisal of one article, whereas synthesis is a summary and critical appraisal of a topic (which may answer several questions and may cover many articles).
  3. Systematic Reviews (a systematic summary and critical appraisal of original studies) which may or may not include a meta-analysis.
  4. Original Studies.

The upper 3 layers represent “Aggregate Evidence”. This is evidence from secondary sources, that search, summarize and critically appraise original studies (lowest layer of the pyramid).

The layers do not necessarily represent the levels of evidence and should not be confused with Pyramids of EBM-levels (type 3). An Evidence Based guideline can have a lower level of evidence than a good systematic review, for instance.
The present pyramid is only meant to lead the way in the labyrinth of sources. Thus, to speed up to process of searching. The relevance and the quality of evidence should always be checked.

The idea is:

  • The higher the level in the pyramid the less publications it contains (the narrower it becomes)
  • Each level summarizes and critically appraises the underlying levels.

I advice people to try to find aggregate evidence first, thus to drill down (hence the drill in the Figure).

The advantage: faster results, lower number to read (NNR).

During the first courses I gave, I just made a pyramid in Word with the links to the main sources.

Our library ICT department converted it into a HTML document with clickable links.

However, although the pyramid looked quite complex, not all main evidence sources were included. Plus some sources belong to different layers. The Trip Database for instance searches sources from all layers.

Our ICT-department came up with a much better looking and better functioning 3-D pyramid, with databases like TRIP in the sidebar.

Moving the  mouse over a pyramid layer invokes a pop-up with links to the databases belonging to that layer.

Furthermore the sources included in the pyramid differ per specialty. So for the department Gynecology we include POPLINE and MIDIRS in the lowest layer, and the RCOG and NVOG (Dutch) guidelines in the EBM-guidelines layer.

Together my colleagues and I decide whether a source is evidence based (we don’t include UpToDate for instance) and where it  belongs. Each clinical librarian (we all serve different departments) then decides which databases to include. Clients can give suggestions.

Below is a short You Tube video showing how this pyramid can be used. Because of the rather poor quality, the video is best to be viewed in full screen mode.
I have no audio (yet), so in short this is what you see:

Made with Screenr:  http://screenr.com/8kg

The pyramid is highly appreciated by our clients and students.

But it is just a start. My dream is to visualize the entire pathway from question to PICO, checklists, FAQs and database of results per type of question/reason for searching (fast question, background question, CAT etc.).

I’m just waiting for someone to fulfill the technical part of this dream.

————–

*Note that there may be different definitions as well. The top layers in the 5S pyramid of Bryan Hayes are defined as follows: syntheses & synopses (succinct descriptions of selected individual studies or systematic reviews, such as those found in the evidence-based journals), summaries, which integrate best available evidence from the lower layers to develop practice guidelines based on a full range of evidence (e.g. Clinical Evidence, National Guidelines Clearinghouse), and at the peak of the model, systems, in which the individual patient’s characteristics are automatically linked to the current best evidence that matches the patient’s specific circumstances and the clinician is provided with key aspects of management (e.g., computerised decision support systems).

Begin with the richest source of aggregate (pre-filtered) evidence and decline in order to to decrease the number needed to read: there are less EBM guidelines than there are Systematic Reviews and (certainly) individual papers.




Searching Skills Toolkit. Finding the Evidence [Book Review]

4 03 2010

Most books on Evidence Based Medicine give little attention to the first two steps of EBM: asking focused answerable questions and searching the evidence. Being able to appraise an article, but not being able to find the best evidence may be challenging and frustrating to the busy clinicians.

Searching Skills Toolkit: Finding The Evidence” is a pocket-sized book that aims to instruct the clinician how to search for evidence. It is the third toolkit book in the series edited by Heneghan et al. (author of the CEBM-blog Trust the Evidence). The authors Caroline de Brún and Nicola Pearce Smith are experts in searching (librarian and information scientist respectively).

According to the description at Wiley’s, the distinguishing feature of this searching skills book,  is its user-friendliness. “The guiding principle is that readers do not want to become librarians, but they are faced with practical difficulties when searching for evidence, such as lack of skills, lack of time and information overload. They need to learn simple search skills, and be directed towards the right resources to find the best evidence to support their decision-making.”

Does this book give guidance that makes searching for evidence easy? Is this book the ‘perfect companion’ to doctors, nurses, allied health professionals, managers, researchers and students, as it promises?

I find it difficult to answer, partly because I’m not a clinician and partly because, being a medical information specialist myself, I would frequently tackle a search otherwise.

The booklet is in pocket-size, easy to take along. The lay-out is clear and pleasant. The approach is original and practical. Despite its small size, the booklet contains a wealth of information. Table one, for instance, gives an overview of truncation symbols, wildcards and Boolean operators for Cochrane, Dialog, EBSCO, OVID, PubMed and Webspirs (see photo). And although this is mouth watering for many medical librarians one wonders whether this detailed information is really useful for the clinician.

Furthermore 34 pages of the 102 (1/3) are devoted on searching these specific health care databases. IMHO of these databases only PubMed and the Cochrane Library are useful to the average clinician. In addition most of the screenshots of the individual databases are too small to read. And due to the PubMed Redesign the PubMed description is no longer up-to-date.

The readers are guided to the chapters on searching by asking themselves beforehand:

  1. The time available to search: 5 minutes, an hour or time to do a comprehensive search. This is an important first step, which is often not considered by other books and short guides.
    Primary sources, secondary sources and ‘other’ sources are given per time available. This is all presented in a table with reference to key chapters and related chapters. These particular chapters enable the reader to perform these short, intermediate or long searches.
  2. What type of publication he is looking for: a guideline, a systematic review, patient information or an RCT (with tips where to find them).
  3. Whether the query is about a specific topic, i.e. drug or safety information or health statistics.

All useful information, but I would have discussed topic 3 before covering EBM, because this doesn’t fit into the ‘normal’ EBM search.  So for drug information you could directly go to the FDA, WHO or EMEA website. Similarly, if my question was only to find a guideline I would simply search one or more guideline databases.
Furthermore it would be more easy to pile the small, intermediate and long searches upon each other instead of next to each other. The basic principle would be (in my opinion at least) to start with a PICO and to (almost) always search for secondary searches first (fast), search for primary publications (original research) in PubMed if necessary and broaden the search in other databases (broad search) in case of exhaustive searches. This is easy to remember, even without the schemes in the book.

Some minor points. There is an overemphasis on UK-sources. So the first source to find guidelines is the (UK) National Library of Guidelines, where I would put the National Guideline Clearinghouse (or the TRIP-database) first. And why is MedlinePlus not included as a source for patients, whereas NHS-choices is?

There is also an overemphasis on interventions. How PICO’s are constructed for other domains (diagnosis, etiology/harm and prognosis) is barely touched upon. It is much more difficult to make PICOs and search in these domains. More practical examples would also have been helpful.

Overall, I find this book very useful. The authors are clearly experts in searching and they fill a gap in the market: there is no comparable book on “the searching of the evidence”. Therefore, despite some critique and preferences for another approach, I do recommend this book to doctors who want to learn basic searching skills. As a medical information specialist I keep it in my pocket too: just in case…

Overview

What I liked about the book:

  • Pocket size, easy to take a long.
  • Well written
  • Clear diagrams
  • Broad coverage
  • Good description of (many) databases
  • Step for step approach

What I liked less about it:

  • Screen dumps are often too small to read and thereby not useful
  • Emphasis on UK-sources
  • Other domains than “therapy” (etiology/harm, prognosis, diagnosis) are almost not touched upon
  • Too few clinical examples
  • A too strict division in short, intermediate and long searches: these are not intrinsically different

The Chapters

  1. Introduction.
  2. Where to start? Summary tables and charts.
  3. Sources of clinical information: an overview.
  4. Using search engines on the World Wide Web.
  5. Formulating clinical questions.
  6. Building a search strategy.
  7. Free text versus thesaurus.
  8. Refining search results.
  9. Searching specific healthcare databases.
  10. Citation pearl searching.
  11. Saving/recording citations for future use.
  12. Critical appraisal.
  13. Further reading by topic or PubMed ID.
  14. Glossary of terms.
  15. Appendix 1: Ten tips for effective searching.
  16. Appendix 2: Teaching tips

References

  1. Searching Skills Toolkit – Finding The Evidence (Paperback – 2009/02/17) by Caroline De Brún and Nicola Pearce-smith; Carl Heneghan et al (Editors). Wiley-Blackell BMJ\ Books
  2. Kamal R Mahtani Evid Based Med 2009;14:189 doi:10.1136/ebm.14.6.189 (book review by a clinician)

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