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:




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 www.pedro.org.au for physical therapy. In my personal experience, however, this database is often out of date and not comprehensive
  • Journal scanning services like EvidenceUpdates from mcmaster.ca), 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])

Thus:

(ENDOCRINE DISEASES[MESH] AND SYSTEMATIC REVIEW[TIAB] AND 2009[DP]) NOT 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 (www.tripdatabase.com/).

*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.

References

  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 (laikaspoetnik.wordpress.com)
  4. Experience versus Evidence [1]. Opioid Therapy for Rheumatoid Arthritis Pain. (laikaspoetnik.wordpress.com)




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.

References 

  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 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/)




How will we ever keep up with 75 Trials and 11 Systematic Reviews a Day?

6 10 2010

ResearchBlogging.orgAn interesting paper was published in PLOS Medicine [1]. As an information specialist and working part time for the Cochrane Collaboration* (see below), this topic is close to my heart.

The paper, published in PLOS Medicine is written by Hilda Bastian and two of my favorite EBM devotees ànd critics, Paul Glasziou and Iain Chalmers.

Their article gives an good overview of the rise in number of trials, systematic reviews (SR’s) of interventions and of medical papers in general. The paper (under the head: Policy Forum) raises some important issues, but the message is not as sharp and clear as usual.

Take the title for instance.

Seventy-Five Trials and Eleven Systematic Reviews a Day:
How Will We Ever Keep Up?

What do you consider its most important message?

  1. That doctors suffer from an information overload that is only going to get worse, as I did and probably also in part @kevinclauson who tweeted about it to medical librarians
  2. that the solution to this information overload consists of Cochrane systematic reviews (because they aggregate the evidence from individual trials) as @doctorblogs twittered
  3. that it is just about “too many systematic reviews (SR’s) ?”, the title of the PLOS-press release (so the other way around),
  4. That it is about too much of everything and the not always good quality SR’s: @kevinclauson and @pfanderson discussed that they both use the same ” #Cochrane Disaster” (see Kevin’s Blog) in their  teaching.
  5. that Archie Cochrane’s* dream is unachievable and ought perhaps be replaced by something less Utopian (comment by Richard Smith, former editor of the BMJ: 1, 3, 4, 5 together plus a new aspect: SR’s should not only  include randomized controlled trials (RCT’s)

The paper reads easily, but matters of importance are often only touched upon.  Even after reading it twice, I wondered: a lot is being said, but what is really their main point and what are their answers/suggestions?

But lets look at their arguments and pieces of evidence. (Black is from their paper, blue my remarks)

The landscape

I often start my presentations “searching for evidence” by showing the Figure to the right, which is from an older PLOS-article. It illustrates the information overload. Sometimes I also show another slide, with (5-10 year older data), saying that there are 55 trials a day, 1400 new records added per day to MEDLINE and 5000 biomedical articles a day. I also add that specialists have to read 17-22 articles a day to keep up to date with the literature. GP’s even have to read more, because they are generalists. So those 75 trials and the subsequent information overload is not really a shock to me.

Indeed the authors start with saying that “Keeping up with information in health care has never been easy.” The authors give an interesting overview of the driving forces for the increase in trials and the initiation of SR’s and critical appraisals to synthesize the evidence from all individual trials to overcome the information overload (SR’s and other forms of aggregate evidence decrease the number needed to read).

In box 1 they give an overview of the earliest systematic reviews. These SR’s often had a great impact on medical practice (see for instance an earlier discussion on the role of the Crash trial and of the first Cochrane review).
They also touch upon the institution of the Cochrane Collaboration.  The Cochrane collaboration is named after Archie Cochrane who “reproached the medical profession for not having managed to organise a “critical summary, by speciality or subspecialty, adapted periodically, of all relevant randomised controlled trials” He inspired the establishment of the international Oxford Database of Perinatal Trials and he encouraged the use of systematic reviews of randomized controlled trials (RCT’s).

A timeline with some of the key events are shown in Figure 1.

Where are we now?

The second paragraph shows many, interesting, graphs (figs 2-4).

Annoyingly, PLOS only allows one sentence-legends. The details are in the (WORD) supplement without proper referral to the actual figure numbers. Grrrr..!  This is completely unnecessary in reviews/editorials/policy forums. And -as said- annoying, because you have to read a Word file to understand where the data actually come from.

Bastian et al. have used MEDLINE’s publication types (i.e. case reports [pt], reviews[pt], Controlled Clinical Trial[pt] ) and search filters (the Montori SR filter and the Haynes narrow therapy filter, which is built-in in PubMed’s Clinical Queries) to estimate the yearly rise in number of study types. The total number of Clinical trials in CENTRAL (the largest database of controlled clinical trials, abbreviated as CCTRS in the article) and the Cochrane Database of Systematic Reviews (CDSR) are easy to retrieve, because the numbers are published quaterly (now monthly) by the Cochrane Library. Per definition, CDSR only contains SR’s and CENTRAL (as I prefer to call it) contains almost invariably controlled clinical trials.

In short, these are the conclusions from their three figures:

  • Fig 2: The number of published trials has raised sharply from 1950 till 2010
  • Fig 3: The number of systematic reviews and meta-analysis has raised tremendously as well
  • Fig 4: But systematic reviews and clinical trials are still far outnumbered by narrative reviews and case reports.

O.k. that’s clear & they raise a good point : an “astonishing growth has occurred in the number of reports of clinical trials since the middle of the 20th century, and in reports of systematic reviews since the 1980s—and a plateau in growth has not yet been reached.
Plus indirectly: the increase in systematic reviews  didn’t lead to a lower the number of trials and narrative reviews. Thus the information overload is still increasing.
But instead of discussing these findings they go into an endless discussion on the actual data and the fact that we “still do not know exactly how many trials have been done”, to end the discussion by saying that “Even though these figures must be seen as more illustrative than precise…” And than you think. So what? Furthermore, I don’t really get their point of this part of their article.

 

Fig. 2: The number of published trials, 1950 to 2007.

 

 

With regard to Figure 2 they say for instance:

The differences between the numbers of trial records in MEDLINE and CCTR (CENTRAL) (see Figure 2) have multiple causes. Both CCTR and MEDLINE often contain more than one record from a single study, and there are lags in adding new records to both databases. The NLM filters are probably not as efficient at excluding non-trials as are the methods used to compile CCTR. Furthermore, MEDLINE has more language restrictions than CCTR. In brief, there is still no single repository reliably showing the true number of randomised trials. Similar difficulties apply to trying to estimate the number of systematic reviews and health technology assessments (HTAs).

Sorry, although some of these points may be true, Bastian et al. don’t go into the main reason for the difference between both graphs, that is the higher number of trial records in CCTR (CENTRAL) than in MEDLINE: the difference can be simply explained by the fact that CENTRAL contains records from MEDLINE as well as from many other electronic databases and from hand-searched materials (see this post).
With respect to other details:. I don’t know which NLM filter they refer to, but if they mean the narrow therapy filter: this filter is specifically meant to find randomized controlled trials, and is far more specific and less sensitive than the Cochrane methodological filters for retrieving controlled clinical trials. In addition, MEDLINE does not have more language restrictions per se: it just contains a (extensive) selection of  journals. (Plus people more easily use language limits in MEDLINE, but that is besides the point).

Elsewhere the authors say:

In Figures 2 and 3 we use a variety of data sources to estimate the numbers of trials and systematic reviews published from 1950 to the end of 2007 (see Text S1). The number of trials continues to rise: although the data from CCTR suggest some fluctuation in trial numbers in recent years, this may be misleading because the Cochrane Collaboration virtually halted additions to CCTR as it undertook a review and internal restructuring that lasted a couple of years.

As I recall it , the situation is like this: till 2005 the Cochrane Collaboration did the so called “retag project” , in which they searched for controlled clinical trials in MEDLINE and EMBASE (with a very broad methodological filter). All controlled trials articles were loaded in CENTRAL, and the NLM retagged the controlled clinical trials that weren’t tagged with the appropriate publication type in MEDLINE. The Cochrane stopped the laborious retag project in 2005, but still continues the (now) monthly electronic search updates performed by the various Cochrane groups (for their topics only). They still continue handsearching. So they didn’t (virtually?!) halted additions to CENTRAL, although it seems likely that stopping the retagging project caused the plateau. Again the author’s main points are dwarfed by not very accurate details.

Some interesting points in this paragraph:

  • We still do not know exactly how many trials have been done.
  • For a variety of reasons, a large proportion of trials have remained unpublished (negative publication bias!) (note: Cochrane Reviews try to lower this kind of bias by applying no language limits and including unpublished data, i.e. conference proceedings, too)
  • Many trials have been published in journals without being electronically indexed as trials, which makes them difficult to find. (note: this has been tremendously improved since the Consort-statement, which is an evidence-based, minimum set of recommendations for reporting RCTs, and by the Cochrane retag-project, discussed above)
  • Astonishing growth has occurred in the number of reports of clinical trials since the middle of the 20th century, and in reports of systematic reviews since the 1980s—and a plateau in growth has not yet been reached.
  • Trials are now registered in prospective trial registers at inception, theoretically enabling an overview of all published and unpublished trials (note: this will also facilitate to find out reasons for not publishing data, or alteration of primary outcomes)
  • Once the International Committee of Medical Journal Editors announced that their journals would no longer publish trials that had not been prospectively registered, far more ongoing trials were being registered per week (200 instead of 30). In 2007, the US Congress made detailed prospective trial registration legally mandatory.

The authors do not discuss that better reporting of trials and the retag project might have facilitated the indexing and retrieval of trials.

How Close Are We to Archie Cochrane’s Goal?

According to the authors there are various reasons why Archie Cochrane’s goal will not be achieved without some serious changes in course:

  • The increase in systematic reviews didn’t displace other less reliable forms of information (Figs 3 and 4)
  • Only a minority of trials have been assessed in systematic review
  • The workload involved in producing reviews is increasing
  • The bulk of systematic reviews are now many years out of date.

Where to Now?

In this paragraph the authors discuss what should be changed:

  • Prioritize trials
  • Wider adoption of the concept that trials will not be supported unless a SR has shown the trial to be necessary.
  • Prioritizing SR’s: reviews should address questions that are relevant to patients, clinicians and policymakers.
  • Chose between elaborate reviews that answer a part of the relevant questions or “leaner” reviews of most of what we want to know. Apparently the authors have already chosen for the latter: they prefer:
    • shorter and less elaborate reviews
    • faster production ànd update of SR’s
    • no unnecessary inclusion of other study types other than randomized trials. (unless it is about less common adverse effects)
  • More international collaboration and thereby a better use  of resources for SR’s and HTAs. As an example of a good initiative they mention “KEEP Up,” which will aim to harmonise updating standards and aggregate updating results, initiated and coordinated by the German Institute for Quality and Efficiency in Health Care (IQWiG) and involving key systematic reviewing and guidelines organisations such as the Cochrane Collaboration, Duodecim, the Scottish Intercollegiate Guidelines Network (SIGN), and the National Institute for Health and Clinical Excellence (NICE).

Summary and comments

The main aim of this paper is to discuss  to which extent the medical profession has managed to make “critical summaries, by speciality or subspeciality, adapted periodically, of all relevant randomized controlled trials”, as proposed 30 years ago by Archie Cochrane.

Emphasis of the paper is mostly on the number of trials and systematic reviews, not on qualitative aspects. Furthermore there is too much emphasis on the methods determining the number of trials and reviews.

The main conclusion of the authors is that an astonishing growth has occurred in the number of reports of clinical trials as well as in the number of SR’s, but that these systematic pieces of evidence shrink into insignificance compared to the a-systematic narrative reviews or case reports published. That is an important, but not an unexpected conclusion.

Bastian et al don’t address whether systematic reviews have made the growing number of trials easier to access or digest. Neither do they go into developments that have facilitated the retrieval of clinical trials and aggregate evidence from databases like PubMed: the Cochrane retag-project, the Consort-statement, the existence of publication types and search filters (they use themselves to filter out trials and systematic reviews). They also skip other sources than systematic reviews, that make it easier to find the evidence: Databases with Evidence Based Guidelines, the TRIP database, Clinical Evidence.
As Clay Shirky said: “It’s Not Information Overload. It’s Filter Failure.”

It is also good to note that case reports and narrative reviews serve other aims. For medical practitioners rare case reports can be very useful for their clinical practice and good narrative reviews can be valuable for getting an overview in the field or for keeping up-to-date. You just have to know when to look for what.

Bastian et al have several suggestions for improvement, but these suggestions are not always underpinned. For instance, they propose access to all systematic reviews and trials. Perfect. But how can this be attained? We could stimulate authors to publish their trials in open access papers. For Cochrane reviews this would be desirable but difficult, as we cannot demand from authors who work for months for free to write a SR to pay the publications themselves. The Cochrane Collab is an international organization that does not receive subsidies for this. So how could this be achieved?

In my opinion, we can expect the most important benefits from prioritizing of trials ànd SR’s, faster production ànd update of SR’s, more international collaboration and less duplication. It is a pity the authors do not mention other projects than “Keep up”.  As discussed in previous posts, the Cochrane Collaboration also recognizes the many issues raised in this paper, and aims to speed up the updates and to produce evidence on priority topics (see here and here). Evidence aid is an example of a successful effort.  But this is only the Cochrane Collaboration. There are many more non-Cochrane systematic reviews produced.

And then we arrive at the next issue: Not all systematic reviews are created equal. There are a lot of so called “systematic reviews”, that aren’t the conscientious, explicit and judicious created synthesis of evidence as they ought to be.

Therefore, I do not think that the proposal that each single trial should be preceded by a systematic review, is a very good idea.
In the Netherlands writing a SR is already required for NWO grants. In practice, people just approach me, as a searcher, the days before Christmas, with the idea to submit the grant proposal (including the SR) early in January. This evidently is a fast procedure, but doesn’t result in a high standard SR, upon which others can rely.

Another point is that this simple and fast production of SR’s will only lead to a larger increase in number of SR’s, an effect that the authors wanted to prevent.

Of course it is necessary to get a (reliable) picture of what has already be done and to prevent unnecessary duplication of trials and systematic reviews. It would the best solution if we would have a triplet (nano-publications)-like repository of trials and systematic reviews done.

Ideally, researchers and doctors should first check such a database for existing systematic reviews. Only if no recent SR is present they could continue writing a SR themselves. Perhaps it sometimes suffices to search for trials and write a short synthesis.

There is another point I do not agree with. I do not think that SR’s of interventions should only include RCT’s . We should include those study types that are relevant. If RCT’s furnish a clear proof, than RCT’s are all we need. But sometimes – or in some topics/specialties- RCT’s are not available. Inclusion of other study designs and rating them with GRADE (proposed by Guyatt) gives a better overall picture. (also see the post: #notsofunny: ridiculing RCT’s and EBM.

The authors strive for simplicity. However, the real world isn’t that simple. In this paper they have limited themselves to evidence of the effects of health care interventions. Finding and assessing prognostic, etiological and diagnostic studies is methodologically even more difficult. Still many clinicians have these kinds of questions. Therefore systematic reviews of other study designs (diagnostic accuracy or observational studies) are also of great importance.

In conclusion, whereas I do not agree with all points raised, this paper touches upon a lot of important issues and achieves what can be expected from a discussion paper:  a thorough shake-up and a lot of discussion.

References

  1. 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

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#Cochrane Colloquium 2009: Better Working Relationship between Cochrane and Guideline Developers

19 10 2009

singapore CCLast week I attended the annual Cochrane Colloquium in Singapore. I will summarize some of the meetings.

Here is a summary of an interesting (parallel) special session: Creating a closer working relationship between Cochrane and Guideline Developers. This session was brought together as a partnership between the Guidelines International Network (G-I-N) and The Cochrane Collaboration to look at the current experience of guideline developers and their use of Cochrane reviews (see abstract).

Emma Tavender of the EPOC Australian Satellite, Australia reported on the survey carried out by the UK Cochrane Centre to identify the use of Cochrane reviews in guidelines produced in the UK ) (not attended this presentation) .

Pwee Keng Ho, Ministry of Health, Singapore, is leading the Health Technology Assessment (HTA) and guideline development program of the Singapore Ministry of Health. He spoke about the issues faced as a guideline developer using Cochrane reviews or -in his own words- his task was: “to summarize whether guideline developers like Cochrane Systematic reviews or not” .

Keng Ho presented the results of 3 surveys of different guideline developers. Most surveys had very few respondents: 12-29 if I remember it well.

Each survey had approximately the same questions, but in a different order. On the face of it, the 3 surveys gave the same picture.

Main points:

  • some guideline developers are not familiar with Cochrane Systematic Reviews
  • others have no access to it.
  • of those who are familiar with the Cochrane Reviews and do have access to it, most found the Cochrane reviews useful and reliable. (in one survey half of the respondents were neutral)
  • most importantly they actually did use the Cochrane reviews for most of their guidelines.
  • these guideline developers also used the Cochrane methodology to make their guidelines (whereas most physicians are not inclined to use the exhaustive search strategies and systematic approach of the Cochrane Collaboration)
  • An often heard critique of Guideline developers concerned the non-comprehensive coverage of topics by Cochrane Reviews. However, unlike in Western countries, the Singapore minister of Health mentioned acupuncture and herbs as missing topics (for certain diseases).

This incomplete coverage caused by a not-demand driven choice of subjects was a recurrent topic at this meeting and a main issue recognized by the entire Cochrane Community. Therefore priority setting of Cochrane Systematic reviews is one of the main topics addressed at this Colloquium and in the Cochrane Strategic review.

Kay Dickersin of the US Cochrane Center, USA, reported on the issues raised at the stakeholders meeting held in June 2009 in the US (see here for agenda) on whether systematic reviews can effectively inform guideline development, with a particular focus on areas of controversy and debate.

The Stakeholder summit concentrated on using quality SR’s for guidelines. This is different from effectiveness research, for which the Institute of Medicine (IOM) sets the standards: local and specialist guidelines require a different expertise and approach.

All kinds of people are involved in the development of guidelines, i.e. nurses, consumers, physicians.
Important issues to address, point by point:

  • Some may not understand the need to be systematic
  • How to get physicians on board: they are not very comfortable with extensive searching and systematic work
  • Ongoing education, like how-to workshops, is essential
  • What to do if there is no evidence?
  • More transparency; handling conflicts of interest
  • Guidelines differ, including the rating of the evidence. Almost everyone in the Stakeholders meeting used GRADE to grade the evidence, but not as it was originally described. There were numerous variations on the same theme. One question is whether there should be one system or not.
  • Another -recurrent- issue was that Guidelines should be made actionable.

Here are podcasts covering the meeting

Gordon Guyatt, McMaster University, Canada, gave  an outline of the GRADE approach and the purpose of ‘Summary of Findings’ tables, and how both are perceived by Cochrane review authors and guideline developers.

Gordon Guyatt, whose magnificent book ” Users’ Guide to the Medical Literature”  (JAMA-Evidence) lies at my desk, was clearly in favor of adherence to the original Grade-guidelines. Forty organizations have adopted these Grade Guidelines.

Grade stands for “Grading of Recommendations Assessment, Development and Evaluation”  system. It is used for grading evidence when submitting a clinical guidelines article. Six articles in the BMJ are specifically devoted to GRADE (see here for one (full text); and 2 (PubMed)). GRADE not only takes the rigor of the methods  into account, but also the balance between the benefits and the risks, burdens, and costs.

Suppose  a guideline would recommend  to use thrombolysis to treat disease X, because a good quality small RCTs show thrombolysis to be slightly but significantly more effective than heparin in this disease. However by relying on only direct evidence from the RCT’s it isn’t taken into account that observational studies have long shown that thrombolysis enhances the risk of massive bleeding in diseases Y and Z. Clearly the risk of harm is the same in disease X: both benefits and harms should be weighted.
Guyatt gave several other examples illustrating the importance of grading the evidence and the understandable overview presented in the Summary of Findings Table.

Another issue is that guideline makers are distressingly ready to embrace surrogate endpoints instead of outcomes that are more relevant to the patient. For instance it is not very meaningful if angiographic outcomes are improved, but mortality or the recurrence of cardiovascular disease are not.
GRADE takes into account if indirect evidence is used: It downgrades the evidence rating.  Downgrading also occurs in case of low quality RCT’s or the non-trade off of benefits versus harms.

Guyatt pleaded for uniform use of GRADE, and advised everybody to get comfortable with it.

Although I must say that it can feel somewhat uncomfortable to give absolute rates to non-absolute differences. These are really man-made formulas, people agreed upon. On the other hand it is a good thing that it is not only the outcome of the RCT’s with respect to benefits (of sometimes surrogate markers) that count.

A final remark of Guyatt: ” Everybody makes the claim they are following evidence based approach, but you have to learn them what that really means.”
Indeed, many people talk about their findings and/or recommendations being evidence based, because “EBM sells well”, but upon closer examination many reports are hardly worth the name.

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Grey Literature: Time to make it systematic

6 09 2009

Guest author: Shamsha Damani (@shamsha)

Grey literature is a term I first encountered in library school; I remember dubbing it “the-wild-goose-chase search” because it is time consuming, totally un-systematic, and a huge pain altogether. Things haven’t changed much in the grey literature arena, as I found out last week, when my boss asked me to help with the grey literature part of a systematic review.

Let me back up a bit and offer the official definition for grey literature by the experts of the Grey Literature International Steering Committee: “Information produced on all levels of government, academics, business and industry in electronic and print formats not controlled by commercial publishing i.e. where publishing is not the primary activity of the producing body.” Grey literature can include things such as policy documents, government reports, academic papers, theses, dissertations, bibliographies, conference abstracts/proceedings/papers, newsletters, PowerPoint presentations, standards/best practice documents, technical specifications, working papers and more! (Benzies et al 2006). So what is so time consuming about all this? There is no one magic database that will search all these at once. Translation: you have to search a gazillion places separately, which means you have to learn how to search each of these gazillion websites/databases separately. Now if doing searches for systematic reviews is your bread-and-butter, then you are probably scoffing already. But for a newbie like me, I was drowning big time.

After spending what seemed like an eternity to finish my search, I went back to the literature to see why inclusion of grey literature was so important. I know that grey literature adds to the evidence base and results in a comprehensive search, but it is often not peer-reviewed, and the quality of some of the documents is often questionable. So what I dug up was a bit surprising. The first was a Cochrane Review from 2007 titled “Grey literature in meta-analyses of randomized trials of health care interventions (review).” The authors concluded that not including grey literature in meta-analyses produced inflated results when looking at treatment effects. So the reason for inclusion of grey literature made sense: to reduce publication bias. Another paper published in the Bulletin of the World Health Organization concluded that grey literature tends to be more current, provides global coverage, and may have an impact on cost-effectiveness of various treatment strategies. This definitely got my attention because of the new buzzword in Washington: Comparative Effectiveness Research (CER). A lot of the grey literature is comprised of policy documents so it definitely has a big role to play in systematic reviews as well. However, the authors also pointed out that there is no systematic way to search the grey literature and undertaking such a search can be very expensive and time consuming. This validated my frustrations, but gave no solutions.

When I was struggling to get through my search, I was delighted to find a wonderful resource from the Canadian Agency for Drugs and Technologies in Health. They have created a document called “Grey Matters: A Practical Search Tool for Evidence-Based Medicine”, which is a 34-page checklist of many of the popular websites for searching grey literature, including a built-in documentation system. It was still tedious work because I had to search a ton of places, many resulting in no hits. But at least I had a start and a transparent way of documenting my work.

However, I’m still at a loss for why there are no official guidelines for librarians to search for grey literature. There are clear guidelines for authors of grey literature. Benzies and colleagues give compelling reasons for inclusion of grey literature in a systematic review, complete with a checklist for authors! Why not have guidelines for searching too? I know that every search would require different tools; but I think that a master list can be created, sort of like a must-search-these-first type of a list. It surely would help a newbie like me. I know that many libraries have such lists but they tend to be 10 pages long, with bibliographies for bibliographies! Based on my experience, I would start with the following resources the next time I encounter a grey literature search:

  1. National Guideline Clearinghouse
  2. Centre for Reviews and Dissemination
  3. Agency for Healthcare Research and Quality (AHRQ)
  4. Health Technology Assessment International (HTAI)
  5. Turning Research Into Practice (TRIP)

Some databases like Mednar, Deep Dyve, RePORTer, OAIster, and Google Scholar also deserve a mention but I have not had much luck with them. This is obviously not meant to be an exhaustive list. For that, I present my delicious page: http://delicious.com/shamsha/greylit, which is also ever-growing.

Finally, a request for the experts out there: if you have any tips on how to make this process less painful, please share it here. The newbies of the world will appreciate it.

Shamsha Damani

Clinical Librarian

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