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)
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Another bug in My NCBI?

15 10 2008

This bug is now fixed (15-11-2008) !!!

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It is confusing, but each week I have another post on the appearance, disappearance or reappearance of a bug in PubMed’s My NCBI:

For me this is an essential feature of My Collections.Often, when I develop a sensitive search, I collect all relevant studies, especially the ones that were not in my search (i.e. found by checking references or ‘related articles’). Then I optimize the search and hope all the relevant records will be found. This can be checked by combining (a) search(es) with the collection(s). If the search is good all relevant records will be found.

Of course this will only work when you CAN combine the collection from My NCBI with one or more searches in the History.

A cumbersome solution, that only works for one collection at the time, is that you send the collections (executed in PubMed) to the Clipboard and combine this set (#0) with the searches, but I prefer a simpler solution. In fact it has always been possible in the past….

Well we will write again to the help desk.
Hopefully I will report the bug repair next week and there will be no follow up.

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Voor de tweede keer een bug in My NCBI. Dit keer gaat het om “My Collections”. Als je een “collection” activeert, worden de desbetreffende records (in het voorbeeld 39 items) wel uitgevoerd in PubMed, maar komen ze niet in de History terecht.

Dat vind ik erg vervelend, omdat ik My Collections vooral gebruik om uitgebreide zoekacties op te zetten.

Ik sla alle relevante artikelen op in My Collections en voer ze op een later tijdstip uit. Dan combineer ik ze met een of meer searches. Ik kan zo checken of ik met zo’n search alle relevante artikelen (bijv. gekregen van klant of via related articles) vind. Is dat niet het geval, dan is het een manier om ontbrekende termen te vinden.

Deze procedure werkt nu dus niet meer, omdat een set uit My Collections niet in de History terechtkomt.

Ik heb wel een voorlopige kunstgreep bedacht, t.w. deze items in Pubmed naar het Clipboard sturen, zodat ze alsnog als set #0 in de History komen te staan. Dat werkt natuurlijk maar met 1 set tegelijk en is tamelijk omslachtig.

Voorheen werkte dit trouwens wel altijd, dus het zal wel weer liggen aan de overhaaste ‘reparaties’ en aanpassingen.

Nou, dat wordt weer een mailtje richting helpdesk.

Hopelijk wordt het snel verholpen en hoort u even niet meer van mij..





Bug My NCBI repaired

8 10 2008

I’m pleased to announce that the bug in PubMed’s My NCBI, that I pointed out a week ago, has been repaired.

For two weeks, since the update of My NCBI, searches comprised of setnumbers were incorrectly saved in My NCBI, thus literally as #1 AND #2, or in the example I gave as: #3 + RCT filter instead of: hirsutism and spironolactone (+ RCT-filter), which was the actual search behind it. (see Figures below)

This was the response I just received from someone of the U.S.National Library of Medicine:

“You can now save searches with search statement (aka History) numbers. Unfortunately, any that you recently created that didn’t work are not going to work, so please delete those.

As part of the fix, we made some changes to how links for saved search names work in My NCBI. On the screen where you used to see “View Results,” use the search name to link to run the search in PubMed. The “Edit” link now takes you to where you can change the specs of the search. These changes are not yet finished. When we have things running normally we will provide more detailed information in our newsletter.

Thank you for your patience.”

I’ve checked it and it really works. Thank god it does. It is really an essential feature, especially for the unexperienced searcher: the (correct number of) brackets are automatically in the right positions.

I’m also pleased with the way the saved searched are presented. It is far more logic that the search is executed when clicking at the underlined name (which looks like a link) and that you can edit where it says “edit”.

I’m looking forward to the other enhancements.

search was erroneously saved as (#3) AND ....

Search is now saved as: (((hirsutism) AND (spironolactone) AND ....

The old (wrong) and updated search in My NCBI (in the new layout)





About “1 AND 2 = 3” in My NCBI

1 10 2008

The PubMed My NCBI feature has been updated. The navigation is entirely different and -in my view- less intuitive and more complex. The increased complexity may relate to the new features, some seeming rather unnecessary (filters), others looking promising: my bibliography, persistent cookies, no limit to the number of saved searches or collections per account (hurray!).

You can find details about the My NCBI changes in the NLM-bulletin and in MyNCBI-help.

For now, I just want to address one point, that hopefully is a “temporary error”.

I noticed it last Friday, thought that it was just a technical error of the kind that frequently occurs these days in PubMed, but will be restored without any notice.

But the mistake (?) is still there. It is about HOW PubMed searches are saved

Before, if you combined two sets, say: “#1 AND #2”, set #3 would be created: #1 AND #2.
If you would save #3 in My NCBI, you would save the entire search behind #1 AND #2, but now only the string “#1 AND #2” is saved. You can easily imagine that set numbers #1 AND #2 are only meaningful if #1 AND #2 are still present and the same as in the original search.
A Dutch colleague just shouted out he got an error message when trying to execute a saved search. Set X was not recognized….

Example.

Suppose you want to find an answer to the following question: Is spironolactone useful (compared to cyproterone acetate for instance) to reduce hirsutism in women with PCOS?

You search for:

  • hirsutism (#1) and spironolactone (#2) (checking that these are mapped to the appropriate MeSH using Details)
  • combine the two sets with AND.
  • Subsequently combine #3 with a narrow filter for the Therapy Domain (filter for RCT’s) in the Clinical Queries.
  • Set #4 (=#3 AND filter) gives 23 results.
  • You save set #4 in My NCBI.
  • But what happens:
    It is saved as #3 AND filter, not as: hirsutism AND spironolactone AND filter.
    Reexecuting the search if the original History is gone yields 0 results (or an erroneous result).

Personally I can circumvent most problems, because I optimize my searches in Word (also nice as safeguard when the PubMed servers are overheated), but for most users this is an unnecessary extra step.

I hope this bug (?, I hope it is a bug) is quickly restored by NLM.

Please inform them by writing to the PubMed helpdesk (at the bottom of the PubMed front page). I will do the same.





Blog Spam and Spam Blogs (2)

14 09 2008

In a previous post I gave two examples of Health Blogs that are really pills-selling-sites. In this post I will show two examples of real Spam Blogs.

Spam blogs or splogs are usely fake weblogs where content is often either inauthentic text or merely stolen (scraped) from other websites. All spam artificially increases the site’s search engine ranking, increasing the number of potential visitors.

Database-management blog: no longer exists

Original post at this blog above and comment below.

One Spam blog that I wanted to show you, is no longer available. It is called Database Management.

Technorati-profile (authority=51)

This blog had no own content, but scraped it from blogposts having the (WordPress?) tag “database”. Although the post does link to the original site, it doesn’t refer to the author’s proper name, but some automatically generated fake name. For instance Shamisos instead of Laikaspoetnik (see Fig).

When I tried to place a comment on their site I had to login into the WordPress-account (although I was already logged in into mine). That’s when I began to really distrust it.

It’s technorati profile still exists (see Fig.). It is clear that the blog has rapidly increased it’s “authority” in the few months it existed. From zero to 51.
Many blogs linking to this blog are also gone or peculiar. Other blogs might have just linked to the spam blog because they assumed that this was the original post, not the copy. Presumably by having so much content on ‘database management’ the splog gets more traffic (of the preferred kind). This might be an example of a splog that backlinks to a portfolio of affiliate websites, to artificially inflate paid ad impressions from visitors, and/or as a link outlet to get new sites indexed (Wikipedia).

The second example of a spamblog is a very interesting site for Medical Librarians: Generic Pub, with the webadress: http://genericpubmed.com/pub/ with posts about PubMed. Really high quality information. Why? Because the posts derive from elsewhere. All of my posts about PubMed are in there, as are those of my colleagues, and perhaps your posts as well. There is no clue as to where the post really came from. You don’t get any pingbacks, unless the (original) post linked to you. That’s how I found out. As with the other spamblogs you cannot comment. Comments are always closed.

one of my posts on Generic Pub

The blogroll of Generic Pub

Blogroll of Generic Pub

Generic PubMed homepage

Generic PubMed homepage

The site does not hide its real intentions. To the left is a huge pill “cialis” and the blogroll consists of only pills, as well as PubMed tag feeds of Technorati and WordPress.

If you strip of the web adress to: http://genericpubmed.com you arive at the homepage, which is unmistakingly a pharmaceutical e-commerce website. Why is this done? Perhaps the sites looks more reliable whith all those PubMed posts or perhaps the site might be easier to find.

One way or another, these two sites steal posts from other sites. Tags used by Technorati or by WordPress, that can be easily transformed into a feed make it very easy for these spambloggers to automatically import blogposts with a certain tag.
By the way, did you find your post in there?

Previous post, see here.

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Database-management blog: no longer exists

In een eerder post heb ik 2 voorbeelden gegeven van blogs die eigenlijk tot doel hebben pillen te verkopen.

Nu 2 voorbeelden van echte Spam Blogs.

Volgens Wikipedia: Spam blogs of splogs zijn doorgaans nep-weblogs, waarvan de inhoud vaak min of meer gestolen wordt (“scraped”) van andere websites. Dit verhoogt de ranking door zoekmachines en zorgt ervoor dat het aantal bezoekers toeneemt.

Een Spam blog dat ik jullie wilde laten zien, is niet langer beschikbaar, tw. Database Management.

Dit blog had alle inhoud gepikt van posts met de (WordPress?) tag “database”. Er wordt wel gelinkt naar de originele site, maar de naam van de auteur wordt vervangen door een of andere automatisch gegenereerde naam, bijv. Shamisos in plaats van Laikaspoetnik (see Fig in engelstalig gedeelte).

Toen ik een commentaar wilde plaatsen op deze site, werd ik gedwongen in te loggen in WordPress, terwijl ik nota bene al ingelogd was. Vanaf dat moment vertrouwde ik het echt niet meer.

Het technorati profiel van deze site bestaat nog steeds (zie fig in engelstalig gedeelte). Het blog is in enkele maanden tijd van 0,0 tot 51 gestegen in “authoriteit”.
Veel blogs die naar dit blog linken zijn ook opgeheven of zijn verdacht. Andere blogs hebben misschien slechts per ongeluk naar deze splog gelinked, omdat men dacht met de originele post van doen te hebben, niet de kopie. Waarschijnlijk krijgt de splog zo meer verkeer van mensen die juist in database management geinteresseerd zijn. Mogelijk is dit een splog die teruglinkt naar een aantal klonen en vice versa. (Wikipedia).

Het 2e voorbeeld van een splog is een erg interessante site voor medisch informatiespecialisten, nl Generic Pub met het webadres: genericpubmed.com/pub. Allemaal kwalitatief zeer goede posts over PubMed. Maar ze zijn wel gejat. Al mijn berichten met de tag PubMed zijn er te vinden, evenals die van mijn collega’s en misschien uw berichten ook wel.
Nergens is de ware herkomst van de berichten te herleiden. De echte auteurs krijgen normaal geen pingback, alleen als de oorspronkelijke post een link naar hen bevat. Zo kwam ik er eigenlijk achter. Evenals de andere splogs, kun je geen commentaar plaatsen.

De website verhult zijn werkelijke bedoelingen niet. Links staat een reuzachtige pil “cialis” en de blogroll bevat alleen namen van pillen alsmede de feeds van de PubMed tags van Technorati en WordPress.
Als je het webadres stript tot: genericpubmed.com kom je op de homepage, onmiskenbaar een e-commerce site. Waarom verschuilt men zich achter zo’n blog? Lijkt de site er betrouwbaarder door of vinden potentiele klanten de site makkelijker?

Hoe dan ook deze 2 sites stelen van andere websites. Een feed nemen op Technorati- of WordPress-tags is een eitje, en dit maakt het deze spambloggers erg makkelijk om automatisch blogposts met een bepaalde tag te importeren.
Tussen 2 haakjes, heeft u uw post al getraceerd?

Vorig bericht in deze serie, zie hier.





PubMed Search Clinic on ATM, Citation Sensor, Advanced Search: Video available.

21 07 2008

The video from the online Search clinic on recent PubMed changes, announced in a previous post is now available at: nlm.nih.gov (pmupdate08): click here.

Direct link to the video only: https://webmeeting.nih.gov/p91519064/

A good coverage is given by Michelle Kraft (Krafty Librarian) at her site (click here).

The clinic, presented by Katherine Majewski, updated recent changes to PubMed, earlier described at the NLM information bulletins on the new ATM and the Beta Advanced Search page.
Recent changes have also been amply described (and discussed) at several of my previous posts, most notably this one.

Here is an overview, with emphasis on new aspects (at least to me).

Citation Sensor:

In the clinic the citation sensor was defined as: “a new feature designed for users seeking specific citations”. However it is not a separate search box. The citation sensor works automatically when you type words into the general search bar. If combination of words are recognized as representing citations (e.g. volume numbers, author names, journal titles) the matches are displayed in a yellow box above the retrieval.

In my previous post I already discussed that the sensor doesn’t always work perfectly and like Krafty, I think that the Single Citation Matcher (in the blue side bar) performs better. It suggests author and journal titles as you write them. Furthermore, you can just fill in the specific information you know in specific fields, i.e. if the author name is misspelled/wrong, it often suffices to fill in year, page number and title word(s), to name just one possible combination. In response to a question, Majewski said the sensor is not an advantage per se as opposed to the Single Citation Matcher. Probably it is just handy for people used to a Google-like way of searching.

One thing new to me was that there are two “Details” when performing a search.

When you type: choi blood 2008, the citation sensor finds 6 hits, 3 of them shown in the yellow box.
The Details button shows: choi[All Fields] AND (“blood”[Subheading] OR “blood”[All Fields] OR “blood”[MeSH Terms]) AND 2008[All Fields].

However when you click 6 articles to see them all, the Details button shows how the citation sensor has translated the search in: choi[Author] AND (blood[Author] OR “Blood”[Journal]) AND 2008[Publication Date]

Thus in fact the search is translated twice (although the citation sensor-results are always a subset of the full results). If you click on 6 articles, the 2nd translation appears as a 2nd search in the Search History.

ATM – Automatic Term Mapping.

ATM has been changed in conjunction with the citation sensor in order to identify queries that contain citation-type information. The old ATM mapped search terms to subject, journal, and author tables in that order. If a MeSH-match was found, PubMed would search for that MeSH as well the user-input as a textword (title, abstract). Automatic term mapping would then stop because it found a match with MeSH. Thus terms that are not only in the MeSH but also in the author or journal table would have been missed, such as in Burns Laryngoscope 2005. The old ATM would map Burns and Laryngoscope as MeSH (subject-search), but the new ATM also searches these terms in ‘all fields’, thus enabling the retrieval of the paper of Burns in Laryngoscope.
In the Q & A part of the session Majewski advised to use qualifiers as MeSH when Burns is searched just as a topic. I only wonder if/how most of the untrained people would find this out.

Another consequence, not really addressed here, is that multi-term words are split and searched individually. With the new ATM, gene therapy is not only searched as the phrase gene therapy (as MeSH-term and textword) but also as ”gene”[All Fields] AND “therapy”[All Fields], which leads to a far greater retrieval (almost 250%). Few of these extra hits are relevant. (see previous post)

Statistics, however, show that the thousands (‘real’) queries performed returned only 10% extra hits on average (see ATM-FAQ for more information). According to NLM, the enhanced ATM and citation sensor have considerably improved searching PubMed. Probably because most people just come to PubMed to search a specific paper or subject (running one or two search commands). The new features enhance citation searches, while subject searches do not suffer too much as long as multiple terms (concepts) are used, as this will filter much of the noise seen with one term (because the term is searched within the context of the other word).

My remark that most of my patrons do do subject searches was interpreted as “do do broad searches“. Which in effect they do (i.e. searches for systematic reviews), but I do not think the suggested NCBI books might be very helpful to them, although it might indeed serve those people (patients?) that want information about broad subjects like “burns”. Perhaps PubMed/NCBI can offer subject searchers other tools as well.

Notably, based on user input there are now (as of July 2nd) some exceptions to the new ATM-rule:
Substance names (such as ferrous glucanate) and
MeSH with stand alone letters or numbers (like complement factor B) will not be broken apart, but searched as a phrase.

Advanced Search (Beta-version)
Advanced Search is amply discussed in a previous post. However, I didn’t mention that the page consists of 4 collapsible boxes beneath the Search Bar (I missed this: you have to click a small minus sign at the upper left of each box in order to collapse.) In essence you can search by many fields, the default fields displayed being Author, Journal, and Publication Date (box2) and all fields (box 4). There is an index for each selected field available (little buttons right of the search boxes). I see no other difference between box 2 and 4 than the defaulted field and the fact that you can only make multiple choices from the index in box 4. Answering a question in the audience Majewski said they might consider allowing multiple choices in box 2 as well.
Box 3 shows limit-options, much the same as the Limit-tab in the usual frontpage, except that you can unlock your limits to future searches using the lock icon (by defaulted limits are carried to future searches).

Thus again this new ‘enhancement’ mainly facilitates citation searches, not subject searches. Clinical Queries are absent and it is for instance not possible to look up any MeSH other than by index, and even this often goes wrong with multi-word terms. The question why MeSH-trees were unavailable in the beta-version remained unanswered at the clinic.
It was a relief though to hear that there were no intentions to replace the normal PubMed frontpage by this advanced search page in due course.

Katherine Majewski ended the clinic by saying that answers to the questions posed during the clinic would be shown at this NLM-page later. She also encouraged to give positive and negative feecback by writing to the NLM customer service and to be as specific as possible if your search was negatively affected by the recent PubMed changes.

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NL flag NL vlag

De video van de PubMed Search Clinic, die ik in een eerder bericht aankondigde is nu te zien op: http://www.nlm.nih.gov/bsd/disted/clinics/pmupdate08.html.

Directe link naar de video: klik hier

Michelle Kraft (Krafty Librarian) heeft de clinic al goed op haar blog samengevat.

De webpresentatie, gegeven door Katherine Majewski, behandelde de recente PubMed-veranderingen, zoals aangekondigd in de NLM informatiebulletins (gewijzigde ATM-mapping resp. Beta Advanced Search)
Eerder heb ik deze veranderingen ook al uitgebreid beschreven en becommentarieerd. (zie bijv. hier).

Hier een samenvatting, met nadruk op nieuwe aspecten

Citation Sensor:

In de webpresentatie werd de “citation sensor” omschreven als: “a new feature designed for users seeking specific citations”. Het is echter geen aparte zoekoptie. De citation sensor doet zijn werk automatisch als je woorden in de algemene zoekbalk typt. De als citaties herkende hits worden apart op een gele achtergrond getoond.

Eerder heb ik al opgemerkt dat de sensor niet altijd goed werkt en evenals Krafty denk ik dat de Single Citation Matcher (in the blauwe balk) veel beter werkt. Deze geeft nl. woordsuggesties terwijl je typt en je kunt elke mogelijke informatie specifiek invullen. Weet je een auteur niet dan kun je vaak volstaan met jaar, paginanummer en titelwoorden, om maar één combinatie te noemen. Volgens Majewski is de sensor ook niet perse beter. Waarschijnlijk is het vooral handig voor mensen die gewend zijn aan een Google-zoekwijze en die verder weinig weten van PubMed. Zelf zou ik toch wel graag willen dat je de citation sensor naar believen aan of uit kon zetten.

Ik zag nu pas voor het eerst dat je 2 “Details” hebt, als de citatie-sensor iets mapt.

Typ je: choi blood 2008, dan vindt de sensor 6 hits en toont er 3.
Onder Details is te zien dat Pubmed de search vertaald als: choi[All Fields] AND (“blood”[Subheading] OR “blood”[All Fields] OR “blood”[MeSH Terms]) AND 2008[All Fields].

Als je op 6 articles klikt om ze allemaal te zien, staat onder Details hoe de citatie-sensor de search vertaald heeft: choi[Author] AND (blood[Author] OR “Blood”[Journal]) AND 2008[Publication Date]

Dus, er zijn eigenlijk 2 ‘vertaalslagen’ Als je op 6 articles klikt dan verschijnt de 2e mapping als een zoekset in the zoekgeschiedenis.


ATM – Automatic Term Mapping.

ATM is evenals de citatie-sensor ontwikkeld aangepast om zoekacties gericht op het vinden van artikelen te vergemakkelijken. De oude ATM stopte met het zoeken van termen in de MeSH-, auteurs- en tijdschriftenlijst als een passende MeSH was gevonden. Tevens werd het ingetypte woord als tekstwoord gezocht. Met als gevolg dat termen die zowel in de MeSH- als in de auteurs- of tijdschriftenlijst voorkwamen nooit anders dan als MeSH (en tekstwoord) werden gezocht. Met Burns Laryngoscope 2005 zou dus nooit het artikel van Burns in Laryngoscope zijn gevonden. Met de nieuwe ATM lukt dat wel.
Majewski adviseerde om veldenaanduidingen (qualifiers). zoals MeSH te gebruikenals je op een onder onderwerp zoals ‘Burns’ wilt zoeken. Dan vraag je je wel af in hoeverre de gemiddelde Pubmed -gebruiker dit weet.

Tijdens de sessie werd niet echt aangekaart dat termen die uit meerdere woorden bestaan worden opgesplitst en in alle velden worden gezocht. Eerder heb ik al laten zien dat bij de nieuwe ATM 2,5 x meer hits oplevert met een term als gen therapie en dat de meeste van deze hits weinig relevant zijn.

Volgens de NLM statistieken leiden echte zoekacties gemiddels slechts to 10% extra hits (zie ATM-FAQ voor meer info) en zijn zoekacties door de vernieuwingen aanzienlijk verbeterd . Waarschijnlijk omdat de meeste mensen alleen maar snel even iets opzoeken (1-2 zoekopdrachten) en vooral geinteresseerd zijn in specifieke artikelen. Wat dat levert het intypen van wat termen in de zoekbalk nu eerder wat op, en zolang je veel termen met elkaar combineert heb ik ook niet veel last van veel ruis bij het zoeken op onderwerp. Maar ik ben zeker niet overtuigd dat dit het zoeken op onderwerp verbetert.

Mijn opmerking dat mijn klanten vooral op onderwerp zoeken werd opgevat als dat ze vooral breed zoeken. Nu is dat wel zo, maar ik denk niet dat zij veel aan suggesties hebben als NCBI-books. Dit lijkt me wel geschikt voor mensen die zich globaal willen inlezen in een onderwerp als brandwonden (burns), patienten bijvoorbeeld. Misschien heeft PubMed/NCBI wel nog andere tools voor uitputtende searches in het verschiet….

Op basis van gebruikersfeedback zijn er vanaf 2 Juli wel enkele uitzonderingen op de nieuwe ATM-regel, t.w.:
Substance names (zoals ferrous glucanate) en
MeSH with losstaande letters en cijfers worden niet langer opgesplitst, maar als phrase gezocht.

Advanced Search (Beta-versie)
Advanced Search heb ik ook eerder uitgebreid besproken (zie hier). Wat ik nu pas bemerk, is dat de velden onder de zoekregel in-en uitklapbaar zijn. Er is een miniscuul min tekentje helemaal linksboven elk veld, waar je op moet klikken om het veld te verkleinen.

De essentie van advanced search is dat je veel verschillende velden kunt doorzoeken, maar dat de standaard velden weer citatie-gericht zijn, dus: Author, Journal, and Publication Date (veld 2) en All Fields (veld 4). Je kunt termen voor elk gekozen veld opzoeken in een index (klein knopje rechts). Ik zie eigenlijk geen verschil tussen veld 2 en 4, behalve dan het standaard veld en het feit dat je in het 4e veld verschillende termen tegelijk kunt aanklikken. Mogelijk komt deze optie ook voor veld 2.
In veld 3 kun je limieten aanklikken, eigenlijk erg vergelijkbaar met de Limit-Tab op de PubMed openingspagina. Wel prettig dat je een limiet desgewenst alleen gedurende één zoekactie kunt toepassen (default: blijft alle zoekacties aanstaan).

Dus ook advanced search beta is vooral ten dienste van degene die bepaalde artikelen zoekt. Je kunt bijvoorbeeld alleen maar de MeSH in de index opzoeken en er zijn geen Clinical Queries. De vraag waarom De MeSH-hierarchie niet geraagdpleegd kon worden vanuit bleef onbeantwoord.
Het was wel een pak van mijn hart, dat het volgens Majewski niet de bedoeling was dat de Advanced Search de normale openingspagina op termijn zou vervangen.

Katherine Majewski beeindigde de sessie met de mededeling dat antwoorden op gestelde vragen later op deze pagina zou verschijnen.

Ze verzocht iedereen ook hun eventuele problemen met de veranderingen zo specifiek mogelijk aan de help desk door te geven.





PubMed Online Search Clinic on ATM!

17 07 2008

Just a short note at the last moment.

Back from vacation I picked up some twitter and blog messages announcing a PubMed search clinic offered at July 17 (today!) at 2pm Eastern time (8pm Amsterdam/Paris time, see timetable throughout the world).

A 30 minute online search clinic will be presented by the NLM® and the National Training Center and Clearinghouse (NTCC) via Adobe® ConnectTM on Thursday, July 17th (2pm ET). The presentation will cover changes to PubMed including changes to how PubMed handles your search (the new automatic term mapping process), the citation sensor, and the beta Advanced Search page.

There is a maximum capacity of 300 participants, on a first come first served base. However, the clinic will be recorded and will be available for viewing later.

To follow the clinic log in at: https://webmeeting.nih.gov/pmupdate08/

or: http://www.nlm.nih.gov/bsd/disted/clinics/pmupdate08.html.
Here you find more info about the clinic, as well as tips for successful participation in the clinic. Be sure to test it beforehand.

Sources:

The Krafty Librarian: @Krafty (twitter) and several posts on her blog.

Nikki (Eagledawgs) guest post on David Rothman’s blog

Background info on what others have blogged about recent Pubmed can be found on another Krafty Librarian’s post and several of my previous post, including PubMed: Past, Present And Future, PART II

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Even op de valreep.

Net terug van vakantie zag ik enkele twitters en blogberichten die een “PubMed search clinic” aankondigden.

Deze begint om 8 hr p.m. (welke tijd waar?).

Het duurt 30 minuten en gaat over de recente veranderingen in Pubmed, de nieuwe ATM (automatic term mapping), de citation sensor en Advanced Search Beta.

Er kunnen 300 mensen deelnemen, volgens het “wie het eerst komt, het eerst maalt” principe. De clinic wordt wel opgenomen, zodat je hem later nog eens kunt bekijken.

Inloggen voor 19.00: https://webmeeting.nih.gov/pmupdate08/

Meer info op: http://www.nlm.nih.gov/bsd/disted/clinics/pmupdate08.html.
Inclusief tips om de clinic goed te kunnen volgen.

Bronnen:

The Krafty Librarian: @Krafty (twitter) en verschillende blogberichten.

Nikki (Eagledawgs) te gast op het blog van David Rothman.

Achtergrondinfo over wat anderen van de veranderingen vinden zijn ook te vinden de site van Krafty Librarian (zie hier). Enkele van mijn eerdere berichten zoals PubMed: Past, Present And Future, PART II zijn er ook aan gewijd.