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%!


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?


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.


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.


  1. PubMed versus Google Scholar for Retrieving Evidence 2010/06 (
  2. Google scholar for systematic reviews…. hmmmm  2013/01 (
  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 (
  6. What’s Wrong With Google Scholar for “Systematic” Review 2013/01 (
  7. Comments at Gehanno’s paper (
  8. Official Reviewer’s report of Gehanno’s paper [1]: Miguel Garcia-Perez, 2012/09
  9. Authors response to comments  (
  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:

The Scatter of Medical Research and What to do About it.

18 05 2012

ResearchBlogging.orgPaul Glasziou, GP and professor in Evidence Based Medicine, co-authored a new article in the BMJ [1]. Similar to another paper [2] I discussed before [3] this paper deals with the difficulty for clinicians of staying up-to-date with the literature. But where the previous paper [2,3] highlighted the mere increase in number of research articles over time, the current paper looks at the scatter of randomized clinical trials (RCTs) and systematic reviews (SR’s) accross different journals cited in one year (2009) in PubMed.

Hofmann et al analyzed 7 specialties and 9 sub-specialties, that are considered the leading contributions to the burden of disease in high income countries.

They followed a relative straightforward method for identifying the publications. Each search string consisted of a MeSH term (controlled  term) to identify the selected disease or disorders, a publication type [pt] to identify the type of study, and the year of publication. For example, the search strategy for randomized trials in cardiology was: “heart diseases”[MeSH] AND randomized controlled trial[pt] AND 2009[dp]. (when searching “heart diseases” as a MeSH, narrower terms are also searched.) Meta-analysis[pt] was used to identify systematic reviews.

Using this approach Hofmann et al found 14 343 RCTs and 3214 SR’s published in 2009 in the field of the selected (sub)specialties. There was a clear scatter across journals, but this scatter varied considerably among specialties:

“Otolaryngology had the least scatter (363 trials across 167 journals) and neurology the most (2770 trials across 896 journals). In only three subspecialties (lung cancer, chronic obstructive pulmonary disease, hearing loss) were 10 or fewer journals needed to locate 50% of trials. The scatter was less for systematic reviews: hearing loss had the least scatter (10 reviews across nine journals) and cancer the most (670 reviews across 279 journals). For some specialties and subspecialties the papers were concentrated in specialty journals; whereas for others, few of the top 10 journals were a specialty journal for that area.
Generally, little overlap occurred between the top 10 journals publishing trials and those publishing systematic reviews. The number of journals required to find all trials or reviews was highly correlated (r=0.97) with the number of papers for each specialty/ subspecialty.”

Previous work already suggested that this scatter of research has a long tail. Half of the publications is in a minority of papers, whereas the remaining articles are scattered among many journals (see Fig below).

Click to enlarge en see legends at BMJ 2012;344:e3223 [CC]

The good news is that SRs are less scattered and that general journals appear more often in the top 10 journals publishing SRs. Indeed for 6 of the 7 specialties and 4 of the 9 subspecialties, the Cochrane Database of Systematic Reviews had published the highest number of systematic reviews, publishing between 6% and 18% of all the systematic reviews published in each area in 2009. The bad news is that even keeping up to date with SRs seems a huge, if not impossible, challenge.

In other words, it is not sufficient for clinicians to rely on personal subscriptions to a few journals in their specialty (which is common practice). Hoffmann et al suggest several solutions to help clinicians cope with the increasing volume and scatter of research publications.

  • a central library of systematic reviews (but apparently the Cochrane Library fails to fulfill such a role according to the authors, because many reviews are out of date and are perceived as less clinically relevant)
  • registry of planned and completed systematic reviews, such as prospero. (this makes it easier to locate SRs and reduces bias)
  • Synthesis of Evidence and synopses, like the ACP-Jounal Club which summarizes the best evidence in internal medicine
  • Specialised databases that collate and critically appraise randomized trials and systematic reviews, like for physical therapy. In my personal experience, however, this database is often out of date and not comprehensive
  • Journal scanning services like EvidenceUpdates from, which scans over 120 journals, filters articles on the basis of quality, has practising clinicians rate them for relevance and newsworthiness, and makes them available as email alerts and in a searchable database. I use this service too, but besides that not all specialties are covered, the rating of evidence may not always be objective (see previous post [4])
  • The use of social media tools to alert clinicians to important new research.

Most of these solutions are (long) existing solutions that do not or only partly help to solve the information overload.

I was surprised that the authors didn’t propose the use of personalized alerts. PubMed’s My NCBI feature allows to create automatic email alerts on a topic and to subscribe to electronic tables of contents (which could include ACP journal Club). Suppose that a physician browses 10 journals roughly covering 25% of the trials. He/she does not need to read all the other journals from cover to cover to avoid missing one potentially relevant trial. Instead it is far more efficient to perform a topic search to filter relevant studies from journals that seldom publish trials on the topic of interest. One could even use the search of Hoffmann et al to achieve this.* Although in reality, most clinical researchers will have narrower fields of interest than all studies about endocrinology and neurology.

At our library we are working at creating deduplicated, easy to read, alerts that collate table of contents of certain journals with topic (and author) searches in PubMed, EMBASE and other databases. There are existing tools that do the same.

Another way to reduce the individual work (reading) load is to organize journals clubs or even better organize regular CATs (critical appraised topics). In the Netherlands, CATS are a compulsory item for residents. A few doctors do the work for many. Usually they choose topics that are clinically relevant (or for which the evidence is unclear).

The authors shortly mention that their search strategy might have missed  missed some eligible papers and included some that are not truly RCTs or SRs, because they relied on PubMed’s publication type to retrieve RCTs and SRs. For systematic reviews this may be a greater problem than recognized, for the authors have used meta-analyses[pt] to identify systematic reviews. Unfortunately PubMed has no publication type for systematic reviews, but it may be clear that there are many more systematic reviews that meta-analyses. Possibly systematical reviews might even have a different scatter pattern than meta-analyses (i.e. the latter might be preferentially included in core journals).

Furthermore not all meta-analyses and systematic reviews are reviews of RCTs (thus it is not completely fair to compare MAs with RCTs only). On the other hand it is a (not discussed) omission of this study, that only interventions are considered. Nowadays physicians have many other questions than those related to therapy, like questions about prognosis, harm and diagnosis.

I did a little imperfect search just to see whether use of other search terms than meta-analyses[pt] would have any influence on the outcome. I search for (1) meta-analyses [pt] and (2) systematic review [tiab] (title and abstract) of papers about endocrine diseases. Then I subtracted 1 from 2 (to analyse the systematic reviews not indexed as meta-analysis[pt])



I analyzed the top 10/11 journals publishing these study types.

This little experiment suggests that:

  1. the precise scatter might differ per search: apparently the systematic review[tiab] search yielded different top 10/11 journals (for this sample) than the meta-analysis[pt] search. (partially because Cochrane systematic reviews apparently don’t mention systematic reviews in title and abstract?).
  2. the authors underestimate the numbers of Systematic Reviews: simply searching for systematic review[tiab] already found appr. 50% additional systematic reviews compared to meta-analysis[pt] alone
  3. As expected (by me at last), many of the SR’s en MA’s were NOT dealing with interventions, i.e. see the first 5 hits (out of 108 and 236 respectively).
  4. Together these findings indicate that the true information overload is far greater than shown by Hoffmann et al (not all systematic reviews are found, of all available search designs only RCTs are searched).
  5. On the other hand this indirectly shows that SRs are a better way to keep up-to-date than suggested: SRs  also summarize non-interventional research (the ratio SRs of RCTs: individual RCTs is much lower than suggested)
  6. It also means that the role of the Cochrane Systematic reviews to aggregate RCTs is underestimated by the published graphs (the MA[pt] section is diluted with non-RCT- systematic reviews, thus the proportion of the Cochrane SRs in the interventional MAs becomes larger)

Well anyway, these imperfections do not contradict the main point of this paper: that trials are scattered across hundreds of general and specialty journals and that “systematic reviews” (or meta-analyses really) do reduce the extent of scatter, but are still widely scattered and mostly in different journals to those of randomized trials.

Indeed, personal subscriptions to journals seem insufficient for keeping up to date.
Besides supplementing subscription by  methods such as journal scanning services, I would recommend the use of personalized alerts from PubMed and several prefiltered sources including an EBM search machine like TRIP (

*but I would broaden it to find all aggregate evidence, including ACP, Clinical Evidence, syntheses and synopses, not only meta-analyses.

**I do appreciate that one of the co-authors is a medical librarian: Sarah Thorning.


  1. Hoffmann, Tammy, Erueti, Chrissy, Thorning, Sarah, & Glasziou, Paul (2012). The scatter of research: cross sectional comparison of randomised trials and systematic reviews across specialties BMJ, 344 : 10.1136/bmj.e3223
  2. Bastian, H., Glasziou, P., & Chalmers, I. (2010). Seventy-Five Trials and Eleven Systematic Reviews a Day: How Will We Ever Keep Up? PLoS Medicine, 7 (9) DOI: 10.1371/journal.pmed.1000326
  3. How will we ever keep up with 75 trials and 11 systematic reviews a day (
  4. Experience versus Evidence [1]. Opioid Therapy for Rheumatoid Arthritis Pain. (

Experience versus Evidence [1]. Opioid Therapy for Rheumatoid Arthritis Pain.

5 12 2011

ResearchBlogging.orgRheumatoid arthritis (RA) is a chronic auto-immune disease, which causes inflammation of the joints that eventually leads to progressive joint destruction and deformity. Patients have swollen, stiff and painful joints.  The main aim of treatment is to reduce swelling  and inflammation, to alleviate pain and stiffness and to maintain normal joint function. While there is no cure, it is important to properly manage pain.

The mainstays of therapy in RA are disease-modifying anti-rheumatic drugs (DMARDs) and non-steroidal anti-inflammatory drugs (NSAIDs). These drugs primarily target inflammation. However, since inflammation is not the only factor that causes pain in RA, patients may not be (fully) responsive to treatment with these medications.
Opioids are another class of pain-relieving substance (analgesics). They are frequently used in RA, but their role in chronic cancer pain, including RA, is not firmly established.

A recent Cochrane Systematic Review [1] assessed the beneficial and harmful effects of opioids in RA.

Eleven studies (672 participants) were included in the review.

Four studies only assessed the efficacy of  single doses of different analgesics, often given on consecutive days. In each study opioids reduced pain (a bit) more than placebo. There were no differences in effectiveness between the opioids.

Seven studies between 1-6 weeks in duration assessed 6 different oral opioids either alone or combined with non-opioid analgesics.
The only strong opioid investigated was controlled-release morphine sulphate, in a single study with 20 participants.
Six studies compared an opioid (often combined with an non-opioid analgesic) to placebo. Opioids were slightly better than placebo in improving patient reported global impression of clinical change (PGIC)  (3 studies, 324 participants: relative risk (RR) 1.44, 95% CI 1.03 to 2.03), but did not lower the  number of withdrawals due to inadequate analgesia in 4 studies.
Notably none of the 11 studies reported the primary and probably more clinical relevant outcome “proportion of participants reporting ≥ 30% pain relief”.

On the other hand adverse events (most commonly nausea, vomiting, dizziness and constipation) were more frequent in patients receiving opioids compared to placebo (4 studies, 371 participants: odds ratio 3.90, 95% CI 2.31 to 6.56). Withdrawal due to adverse events was  non-significantly higher in the opioid-treated group.

Comparing opioids to other analgesics instead of placebos seems more relevant. Among the 11 studies, only 1 study compared an opioid (codeine with paracetamol) to an NSAID (diclofenac). This study found no difference in efficacy or safety between the two treatments.

The 11 included studies were very heterogeneous (i.e. different opioid studied, with or without concurrent use of non-opioid analgesics, different outcomes measured) and the risk of bias was generally high. Furthermore, most studies were published before 2000 (less optimal treatment of RA).

The authors therefore conclude:

In light of this, the quantitative findings of this review must be interpreted with great caution. At best, there is weak evidence in favour of the efficacy of opioids for the treatment of pain in patients with RA but, as no study was longer than six weeks in duration, no reliable conclusions can be drawn regarding the efficacy or safety of opioids in the longer term.

This was the evidence, now the opinion.

I found this Cochrane Review via an EvidenceUpdates email alert from the BMJ Group and McMaster PLUS.

EvidenceUpdate alerts are meant to “provide you with access to current best evidence from research, tailored to your own health care interests, to support evidence-based clinical decisions. (…) All citations are pre-rated for quality by research staff, then rated for clinical relevance and interest by at least 3 members of a worldwide panel of practicing physicians”

I usually don’t care about the rating, because it is mostly 5-6 on a scale of 7. This was also true for the current SR.

There is a more detailed rating available (when clicking the link, free registration required). Usually, the newsworthiness of SR’s scores relatively low. (because it summarizes ‘old’ studies?). Personally I would think that the relevance and newsworthiness would be higher for the special interest group, pain.

But the comment of the first of the 3 clinical raters was most revealing:

He/she comments:

As a Palliative care physician and general internist, I have had excellent results using low potency opiates for RA and OA pain. The palliative care literature is significantly more supportive of this approach vs. the Cochrane review.

Thus personal experience wins from evidence?* How did this palliative care physician assess effectiveness? Just give a single dose of an opiate? How did he rate the effectiveness of the opioids? Did he/she compare it to placebo or NSAID (did he compare it at all?), did he/she measure adverse effects?

And what is “The palliative care literature”  the commenter is referring to? Apparently not this Cochrane Review. Apparently not the 11 controlled trials included in the Cochrane review. Apparently not the several other Cochrane reviews on use of opioids for non-chronic cancer pain, and not the guidelines, syntheses and synopsis I found via the TRIP-database. All conclude that using opioids to treat non-cancer chronic pain is supported by very limited evidence, that adverse effects are common and that long-term use may lead to opioid addiction.

I’m sorry to note that although the alerting service is great as an alert, such personal ratings are not very helpful for interpreting and *true* rating of the evidence.

I would rather prefer a truly objective, structured critical appraisal like this one on a similar topic by DARE (“Opioids for chronic noncancer pain: a meta-analysis of effectiveness and side effects”)  and/or an objective piece that puts the new data into clinical perspective.

*Just to be clear, the own expertise and opinions of experts are also important in decision making. Rightly, Sackett [2] emphasized that good doctors use both individual clinical expertise and the best available external evidence. However, that doesn’t mean that one personal opinion and/or preference replaces all the existing evidence.


  1. Whittle SL, Richards BL, Husni E, & Buchbinder R (2011). Opioid therapy for treating rheumatoid arthritis pain. Cochrane database of systematic reviews (Online), 11 PMID: 22071805
  2. Sackett DL, Rosenberg WM, Gray JA, Haynes RB, & Richardson WS (1996). Evidence based medicine: what it is and what it isn’t. BMJ (Clinical research ed.), 312 (7023), 71-2 PMID: 8555924
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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
    (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.

* = 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.


  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 ( 2010/10/19/)
  5. 10 + 1 PubMed Tips for Residents (and their Instructors) ( 2009/06/30)
  6. Adding Methodological filters to myncbi ( 2009/11/26/)
  7. Search filters 1. An Introduction ( 2009/01/22/)

Role of Consumer Networks in Evidence Based Health Information

11 11 2009

Guest author: Janet Wale
member of the Cochrane Consumer Network

People are still struggling with evidence or modern medicine – clinicians, patients, health consumers, carers and the public alike. Part of this is because we always thought medicine was based on quality research, or evidence. It is not only that. For evidence to be used most effectively in healthcare systems researchers, clinicians and ‘the existing or potential patients and carers’ have to communicate and resonate with each other – to share knowledge and responsibilities both in developing the evidence and in individual decision making. On the broader population level, this may include consultation but is best achieved by developing partnerships.

The Cochrane Collaboration develops a large number of the published systematic reviews of best evidence on healthcare interventions, available electronically on The Cochrane Library. Systematic reviews are integral to the collation of evidence to inform clinical practice guidelines. They are also an integral part of health technology assessments, where the cost-effectiveness of healthcare interventions is determined for a particular health system.

With the availability of the Internet we are able to readily share information. We are also acutely aware of disadvantage for many of the World’s populations. What this has meant is pooled efforts. Now we have not only the World Health Organization but also The Cochrane Collaboration, Guidelines International Network, and Health Technology Assessment International. What is common among these organizations? They involve the users of health care, including patients, consumers and carers. The latter three organizations have a formal consumer/patient and citizen group that informs their work. In this way we work to make the evidence relevant, accessible and being used. We all have to be discerning whatever knowledge we are given and apply it to ourselves.

This is  a short post on request.
It also appeared as a comment at:

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#CECEM Bridging the Gap between Evidence Based Practice and Practice Based Evidence

15 06 2009

cochrane-symbol A very interesting presentation at the CECEM was given by the organizer of this continental Cochrane meeting, Rob de Bie. De Bie is Professor of Physiotherapy Research and director of Education of the Faculty of Health within the dept. of Epidemiology of the Maastricht University. He is both a certified physiotherapist and an epidemiologist. Luckily he kept the epidemiologic theory to a minimum. In fact he is a very engaging speaker who keeps your attention to the end.


While guidelines were already present in the Middle Ages in the form of formalized treatment of daily practice, more recently clinical guidelines have emerged. These are systematically developed statements which assists clinicians and patients in making decisions about appropriate treatement for specific conditions.

Currently, there are 3 kinds of guidelines, each with its own shortcomings.

  • Consensus based. Consensus may be largely influenced by group dynamics
    Consensus = non-sensus and Consensus guidelines are guidelies.
  • Expert based. Might be even worse than consensus. It can have all kind of biases, like expert and opinion bias or external financing.
  • Evidence based. Guideline recommendations are based on best available evidence, deals with specific interventions for specific populations and are based on a systematic approach.

The quality of Evidence Based Guidelines depends on whether the evidence is good enough, transparent, credible, available, applied and not ‘muddled’ by health care insurers.
It is good to realize that some trials are never done, for instance because of ethical considerations. It is also true that only part of what you read (in the conclusions) has actually be done and some trials are republished several times, each time with a better outcome…

Systematic reviews and qualitatively good trials that don’t give answers.

Next Rob showed us the results of a study ( Jadad and McQuay in J. Clin. Epidemiol. ,1996) with efficacy as stated in the review plotted on the X-axis and the Quality score on the Y-axis. Surprisingly meta-analysis of high quality were less likely to produce positive results. Similar results were also obtained by Suttorp et al in 2006. (see Figure below)

12066264  rob de bie CECEM

Photo made by Chris Mavergames

There may be several reasons why good trials not always give good answers. Well known reasons are  the lack of randomization or blinding. However Rob focused on a less obvious reason. Despite its high level of evidence, a Randomized Controlled Trial (RCT) may not always be suitable to provide good answers applicable to all patients, because RCT’s often fail to reflect the true clinical practice. Often, the inclusion of patients in RCT’s is selective: middle-aged men with exclusion of co-morbidity. Whereas co-morbidity occurs in > 20% of the people of 60 years and older and in >40% of the people of 80 years and older (André Knottnerus in his speech).

Usefulness of a Nested Trial Cohort Study coupled to an EHR to study interventions.

Next, Rob showed that a nested Trial cohort study can be useful to study the effectiveness of  interventions. He used this in conjunction with an EHR (electronic health record), which could be accessed by practitioner and patient.

One of the diseases studied in this way, was Intermittent Claudication. Most commonly Intermittent Claudication is a manifestation of  peripheral arterial disease in the legs, causing pain and cramps in the legs while walking (hence the name). The mortality is high: the 5 year mortality rates are in between those of colorectal cancer and Non-Hodgkin Lymphoma. This is related to the underlying atherosclerosis.

There are several risk factors, some of which cannot be modified, like hereditary factors, age and gender. Other factors, like smoking, diet, physical inactivity and obesity can be tackled. These factors are interrelated.

Rob showed that, whereas there may be an overall null effect of exercise in the whole population, the effect may differ per subgroup.

15-6-2009 3-06-19 CI 1

  • Patients with mild disease and no co-morbidity may directly benefit from exercise-therapy (blue area).
  • Exercise has no effect on smokers, probably because smoking is the main causative factor.
  • People with unstable diabetes first show an improvement, which stabilized after a few weeks due to hypo- or hyperglycaemia induced by the exercise,
  • A similar effect is seen in COPD patients, the exercise becoming less effective because the patients become short of breath.

It is important to first regulate diabetes or COPD before continuing the exercise therapy. By individually optimizing the intervention(s) a far greater overall effect is achieved: 191% improval in the maximal (pain-free) walking distance compared to for instance <35% according to a Cochrane Systematic Review (2007).

Another striking effect: exercise therapy affects some of the prognostic factors: whereas there is no effect on BMI (this stays an important risk factor), age and diabetes become less important risk factors.

15-6-2009 3-35-10 shift in prognostic factors

Because guidelines are quickly outdated, the findings are directly implemented in the existing guidelines.

Another astonishing fact: the physiotherapists pay for the system, not the patient nor the government.

More information can be found on Although the presentation is not (yet?) available on the net, I found a comparable presentation here.

** (2009-06-15) Good news: the program and all presentations can now be viewed at:

New Cochrane Handbook: altered search policies

14 11 2008

cochrane-symbolThe Cochrane Handbook for Systematic Reviews of Interventions is the official document that describes in detail the process of preparing and maintaining Cochrane systematic reviews on the effects of healthcare interventions.

The current version of the Handbook is 5.0.1 (updated September 2008) is available either for purchase from John Wiley & Sons, Ltd or for download only to members of The Cochrane Collaboration (via the Collaboration’s information management system, Archie).
Version 5.0.0, updated February 2008, is freely available in browseable format, here. It should be noted however, that this version is not as up to date as version 5.0.1. The methodological search filters, for instance, are not1989 visual 6 completely identical.

As an information specialist I will concentrate on Chapter 6: Searching for studies.

This chapter consist of the following paragraphs:

  • 6.1 Introduction
  • 6.2 Sources to search
  • 6.3 Planning the search process
  • 6.4 Designing search strategies
  • 6.5 Managing references
  • 6.6 Documenting and reporting the search process
  • 6.7 Chapter information
  • 6.8 References

As the previous versions the essence of the Cochrane searches is to perform a comprehensive (sensitive) search for relevant studies (RCTs) to minimize bias. The most prominent changes are:

1. More emphasis on the central role of the Trial Search Coordinator (TSC) in the search process.
Practically each paragraph summary begins with an advice to consult the TSC, i.e. in 6.1: Cochrane review authors should seek advice from the Trials Search Co-ordinator of their Cochrane Review Group (CRG) before starting a search.

One of the main roles of TSC’s is the assisting of authors with searching, although the range of assistance may vary from advise on to how run searches to designing, running and sending the searches to authors.

I know from experience that most authors have not enough search literacy to be able to satisfactory complete the entire search on their own. Not even all librarians may be equipped to perform such exhaustive searches. That is why the handbook says: “If a CRG is currently without a Trials Search Co-ordinator authors should seek the guidance of a local healthcare librarian or information specialist, where possible one with experience of conducting searches for systematic reviews.”

Another essential core function of the TSC is the development and maintenance of the Specialized Register, containing all relevant studies in their area of interest, and submit this to CENTRAL (The Cochrane Central Register of Controlled Trials) on a quarterly basis”. CENTRAL is the most comprehensive source of reports of controlled trials (~500,000 records), available in “The Cochrane Library” (there it is called CLINICAL TRIALS). CENTRAL is available to all Cochrane Library subscribers, whereas the Specialized Register is only available via the TSC.


Redrawn from the Handbook Fig. 6.3.a: The contents of CENTRAL

2. Therefore Trials registers are an increasingly important source of information. CENTRAL is considered to be the best single source of reports of trials that might be eligible for inclusion in Cochrane reviews. However, other than would be expected (at least by many authors) a search of MEDLINE (PubMed) alone is not considered adequate.

The approach now is: Specialized Registers/CENTRAL and MEDLINE should be searched as a minimum, together with EMBASE if it is available (apart from topic specific databases, snowballing). MEDLINE should be searched from 2005 onwards, since CENTRAL contains all records from MEDLINE indexed with the Publication Type term ‘Randomized Controlled Trial’ or ‘Controlled Clinical Trial’ (a substantial proportion of theses MEDLINE records have been retagged as a result of the work of The Cochrane Collaboration (Dickersin 2002)).

Personally, for non-Cochrane searches, I would rather search the other way around, MEDLINE (OVID) first, than EMBASE (OVID) and finally CENTRAL, and deduplicate the searches afterwards (in Reference Manager for instance). The (Wiley) Cochrane Library is not easy to search (for non-experienced users, i.e. you have to know the MESH beforehand, there is (yet) no mapping). If you start your search in MEDLINE (OVID) you can easily transform it in EMBASE and subsequently CENTRAL (using both MESH and EMBASE keywords as well as textwords)

3. The full search strategies for each database searched need to be included in an Appendix with the total number of hits retrieved by the electronic searches included in the Results section. Indeed the reporting has been very variable, some authors only referring to the general search strategy of the group. This made the searching part less transparent.

4. Two new Cochrane Highly Sensitive Search Strategies for identifying randomized trials in MEDLINE strategies have been developed: a sensitivity-maximizing version and a sensitivity- and precision-maximizing version. These filters (that are to be combined with the subject search) were designed for MEDLINE-indexed records. Therefore, a separate search is needed to find non-indexed records as well. An EMBASE RCT filter is still under development.

These methodological filters will be exhaustively discussed in another post.