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/)
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Finding assigned MeSH terms and more: PubReMiner

24 09 2008

Generally when searching PubMed I use both MeSH and textwords. If you already have some nice articles, either by performing a quick and dirty search or looking at the Related Articles or your colleague gave you one or two, then you can find the MeSH assigned to these papers by looking in citation format (see Fig). However going through a set of articles looking at all indexed terms takes quite some time and one doesn’t easily get an overview of the overall frequency of MeSH in a set of records.

Therefore, Rachel Walden, asked first at Twitter and then at David Rothman’ site (see here):

“What I’d like to do is to be able to enter the PMIDs of several citations and have the tool search MEDLINE via PubMed for the assigned MeSH terms, and return a single list of the terms used by any of the entered citations with a measurement of frequency. For example, if I input PMIDs 16234728, 15674923, and 17443536, the tool would return results telling me that 100% or 3 of 3 use the term “Catheters, Indwelling”, 2 of 3 use “Time Factors,” 1 of the 3 uses “Urination Disorders,” and so on. Although this example uses 3 PMIDs, I’d like to be able to input at least 10, just based on personal experience.”
(PMID is the unique PubMed-identifier, by searching for the PMID you get one specific record.)

The suggestions made by several people were summarized by David in another post (see here). The following 3rd-party PubMed/MEDLINE tools seemed most promising:

Both give useful results. The layout- is very user-friendly.

I tried my own sets of 20 PMIDs (from a systematic review search on “predictive models for in vitro fertilization”) and http://www.docmobi gave this result:


This was the result when I selected: “primary terms only“. If I selected “include secondary terms” I would find female and pregnancy as well, but at rates of 175%. First I didn’t understand, but than I realized that pregnancy occurs as “Pregnancy” and as “Pregnancy Rate”, whereas female occurs as “Female” and as “Infertility, Female”. Therefore Pregnancy and Female can occur more than once as single words in one record (thus accounting for >100%). It is odd however, that important MeSH like Pregnancy and Female (which are really check tags, MeSH that should be assigned to each article that is about pregnancy or females) are not included in the primary list.

Although useful and nicely presented I still not find this ideal:

  • Check tags are not in the list (with “primary terms only”)
  • Publication types and substance names are in neither list.
  • Subheadings are not included. For words not captured by a single MeSH, but by a combination of MeSH and subheadings this is especially important.
    For instance, there is no MeSH for EGFR-inhibitors, you have to use:
    Receptor, Epidermal Growth Factor/antagonists & inhibitors
  • Textwords are not included (not on Rachel’s wish list but on mine)
  • Personally I find the list so simple that I would have find the terms immediately myself.

Coincidentally I found another 3rd party tool which does the job much better (I think): (PubMed) PubReminer, produced by Jan Koster at our hospital (AMC, Amsterdam). I looked at it, because my colleagues and I are going to discuss it today.

This is the procedure I would recommend to find assigned MeSH and more, starting from PubMed (which is not absolutely required, because you can also search directly in PubReMiner)

  • Collect the PMID’s of the papers you want to analyze.
  • If you have selected the papers in PubMed (on the Clipboard, in My collections or in your search set) then Set the Display Tab on: UI List (Unique identifiers), and export these PMID to a textfile by choosing “to Text” from the Send to button. You get a simple list of PMID’s that you can copy/paste to PubReMiner. (not required, but handy)

  • Go to PubReminer, paste the PMID’s in the search box as one string.
  • Click: “Start PubReminer” (if you enter a search you may wish to apply limits)

  • You see the results, in the following columns: Year, Author, Journal, (Text)Word, MeSH, Substance, Country.

  • Afterwards you can decide
    – Whether the words are searched in title only, in Title and Abstract or also in MeSH and RN.
    – which columns are displayed
    – whether similar words are merged or not.
  • You can use selected terms to build up your search in PubReMiner and/or
  • You can export the terms as a text-file (!)
  • And, by the way, you can download a plugin for IE or Firefox (Fig. right)

This tool is not very intuitive, but the result is quite ideal.

You get both a list of MeSH plus their subheadings AND a list of textwords (and substance names).

There are very useful terms in here, for instance logistic models[mesh]. As textwords I should use ‘logistic regression’ or ‘multivariate analysis’. It is only a pity that terms are given individually and spread: the context is lost (‘analysis’ may for instance be too broad, it is only useful in combination with ‘multivariate’ or ‘regression’).
With respect to the MeSH only individual terms are given. ‘Pregnancy’ is mentioned 19 out of 20 times in the MeSH-list. Does this mean I’m missing one paper by searching “Pregnancy”[MesH]? No, because that paper is indexed with the narrower term “Pregnancy Rate”. (and you will find it by exploding Pregnancy).
So the context and the hierarchy are lost here as well.

But it is about as good as one can get.

I’m gonna use this tool (as an adjunct at least). Seems that it has some other potentials as well: look up genes, find the research interest of an author, determine the journal to submit your work to. Well I probably learn that in a few hours. Perhaps I will come back to it later.





PubMed: Past, Present And Future, PART I

11 06 2008

I.The Past:
Extremely simple, yet incredibly difficult

For Part II (discussion ATM, Advanced Search beta: see here).

Searching PubMed has never been easy, not for the advanced searcher nor the beginner.

As an advanced searcher you have (had?) to find your way through the Search Bar, MeSH-database, look for broader, narrower or related terms, know when to explode MeSH, add major topics or subheadings or not, know when to use ‘Links’ or the ‘Search to Sendbox’ to send Searches to PubMed. Know when and how to use Clinical Queries, Limits, Field Codes (nowadays called tags 🙂 ), History, MyNCBI saved searches and collections, linkouts, AND, OR, NOT and so on….

It takes some investment of time to become an effective & advanced PubMed searcher.

For the less experienced and/or more rushed people (busy clinicians, young investigators, lab-people) trying to find an answer to medical questions, the search bar often sufficed. Here you just typed in some words that were not only searched in title and abstract, but also translated into the corresponding MeSH (if recognized as synonyms), a process called automatic mapping. People just haved to check “Details” to verify proper mapping. It often went well, but sometimes the mapping was completely wrong (i.e. typ: walking aids and you will search for the MeSH term for AIDS, although HIV has nothing to do with it).

The overwhelming number of hits could be effectively reduced with some risk of loosing relevant hits by using the Limits option or using Methodological Filters in the Clinical Queries (EBM). Because of the disease-oriented MeSH, PubMed is not very well suited for preclinical or basic scientific searches. This often leads to frustrations (see below).

Some people just want to look up citations and there is a perfect tool for it: the Single Citation Mapper. It is so wonderful, just type in an author, the journal, first page or whatever. It has an autofill function, so I even prefer this tool to find a journal or an author (instead of the indexes, which is yet another option).

Now let alone the summing up of these possibilities makes me see stars. After a course PubMed a student who knew a lot about programming, sighed:

Wow, there is a lot of stuff in there, but it is all so concealed and difficult to find….”

That’s true, and this together with the superficial resemblance of the search bar with the Google search bar makes inexperienced users use PubMed in a Googlish way: just typ in some words ….. and you probably… don’t find what you want. This was especially true for people looking up a particular paper and not familiar with the Single Citation Mapper, hidden in the blue side bar. (The picture left is from “Arts in Spe”, with the Title: Searching like in Google”)

Or as the Harvard PhD student Anna Kushnir expressed her frustations when ranting against PubMed :

“I hate PubMed. I hate it with a burning passion. For a site that is as vital to scientific progress as PubMed is, their search engine is shamefully bad. It’s embarrassingly, frustratingly, painfully bad. (…)

… I can hold a paper in my hands, search for two authors’ last names and have PubMed come up with nothing. (….)

Why is PubMed so behind the times? Why? How does it even work? Does it search only the abstract? Does it also search the body of the papers that are available online? Why does it get so massively confused by an author’s initials and last name together, in one search. […]

I don’t think I should have to be, or enlist the services of, a medical librarian in order to do a simple search on a literature search engine. PubMed should be an intuitive search engine such as Google, or others. […] PubMed should be tuned to my needs and my skill set. I should not have to tune to it. […]”

There was an overwhelming response to her post and Anna’s story was covered in many blogs. I don’t want to revive that discussion, just want to mention Graham Steele’s comment.

“@ Anna,

You might just be in luck thanks to voicing your frustrations online !!

I brought this post to the attention of Dr Lipman who I’ve just heard back from.

He’s authorized me to post here on his behalf. (Thanks Dr Lipman)

Although the current engine works well for some users and some queries, I understand Anna’s frustration and we are in the midst of a number of changes that will make PubMed work better for her and many other users.

We will be adding a number of other “sensors” which will run in parallel with the default search. From monitoring results of enhancements we’ve added to some of our other Entrez databases.

A number of these complaints are fair and we’ll be doing our best to address them. With the large number of users we have, it will be clear what areas we’ll improving and what areas will need more work.”

I’m now beginning to understand what Graham Steel meant in his reply to Anna.

Coincidantly or not, PubMed has introduced a couple of changes that seem to concede Anna’s demands. This will be the subject of the second part of this Trilogy, see HERE

—————-

NL flag NL vlag

I.The Past:Extremely simple, yet incredibly difficult

Zoeken in PubMed is nooit makkelijk geweest, voor wie dan ook, beginner of gevorderde.

Als je echt volop gebruik wil maken van PubMed dan moet je niet alleen overweg kunnen met de zoekbalk, maar ook met de MeSH database. Je moet weten wat bredere en nauwere termen zijn, weten wanneer automatische explosie gewenst is of niet, wanneer je major topics gebruikt, subheading toevoegt en of je MeSH-termen via ‘Links’ of via de ‘Search to Sendbox’ naar PubMed ‘brengt’. Je moet weten of en hoe je Clinical Queries, Limits, Field Codes (tags), de History, MyNCBI saved searches and collections, linkouts, AND, OR, NOT enzovoorts, enzoverder gebruikt….

Het duurt dus even voor je alle ins en outs kent en op een effectieve manier van de geavanceerde mogelijkheden van PubMed gebruik maakt.

Voor de minder gevorderde gebruikers of de mensen die snel een antwoord willen op een (bio)medische vraag zoals artsen in de drukke klinische praktijk, fundamentele wetenschappers, preklinici voldeed vaak de zoekbalk. Je kon hier gewoon wat woorden intypen en ongezien zoekt (zocht) PubMed niet alleen in titel en abstract, maar vertaalde ze woorden ook in MeSH (als ze als synoniem herkend worden). Dit heet (automatic term) mapping of ATM. Makkelijk, maar het is wel aan te bevelen de “Details”-tab te bekijken om te zien of de search goed vertaald wordt. Soms gaat het namelijk helemaal fout. Bijv. als je walking aids typt, wordt o.a. op de MeSH voor de ziekte Aids gezocht, terwijl dat er natuurlijk niets mee van doen heeft.

Om de enorme hoeveelheid hits te reduceren kun je Limits of Methodologische Filters in de Clinical Queries (EBM-vragen) toepassen. Omdat de MeSH nogal georienteerd zijn op ziekten, is PubMed niet bij uitstek geschikt voor niet-medische vragen. Dit kan nog wel eens tot frustaties leiden. (zie onder)

Wanneer je alleen maar bepaalde artikelen wil opzoeken, kun je dat heel handig doen via de Single Citation Mapper. Typ gewoon de naam van een auteur, het tijdschrift, het pagina- of volumenummer, en/of een titelwoord in. En het artikel is zo gevonden.

Bij het opsommen van al deze mogelijkheden gaat het me al duizelen. Hoe moet het dan op beginners overkomen?

Na een PubMedcursus verzuchtte een student met veel ervaring in programmeren tegen mij.

“Wat een mogelijkheden, maar het is wel heel erg verborgen allemaal en erg moeilijk te vinden. Niet erg gebruikersvriendelijk.

Dat is zondermeer waar en omdat de PubMed zoekbalk oppervlakkig gezien wel op Google lijkt gaan onervaren zoekers (en met name de Google-generatie) erin zoeken als in Google. Ze typen de hele zoekstrategie gewoon in en verwachten dan snel wat te vinden. Helaas is dat niet zo. Zeker specifieke artikelen kon men zo vaak juist niet vinden, omdat wel automatisch met MeSH gemapt werd, maar meestal (juist niet) in het tijdschrift- of auteursveld gezocht werd. Daar was nu juist die handige Single Citation Mapper voor. Veel mensen kennen die echter niet, want de naam is nietszeggend en de optie zit in de blauwe zijbalk verscholen.

Ook promovenda Anna Kushnir liep hier tegenop en blies daarover stoom af op haar blog:

“I hate PubMed. I hate it with a burning passion. For a site that is as vital to scientific progress as PubMed is, their search engine is shamefully bad. It’s embarrassingly, frustratingly, painfully bad. (…)

… I can hold a paper in my hands, search for two authors’ last names and have PubMed come up with nothing. (….)

Why is PubMed so behind the times? Why? How does it even work? Does it search only the abstract? Does it also search the body of the papers that are available online? Why does it get so massively confused by an author’s initials and last name together, in one search. […]

I don’t think I should have to be, or enlist the services of, a medical librarian in order to do a simple search on a literature search engine. PubMed should be an intuitive search engine such as Google, or others. […] PubMed should be tuned to my needs and my skill set. I should not have to tune to it. […]”

Dit blog heeft heel wat losgemaakt, zowel onder voor- en tegenstanders. Ik zal nu niet het stof weer doen opwaaien, maar ik wil alleen nog even Graham Steele’s commentaar vermelden.

@ Anna,

You might just be in luck thanks to voicing your frustrations online !!

I brought this post to the attention of Dr Lipman who I’ve just heard back from.

He’s authorized me to post here on his behalf. (Thanks Dr Lipman)

Although the current engine works well for some users and some queries, I understand Anna’s frustration and we are in the midst of a number of changes that will make PubMed work better for her and many other users.

We will be adding a number of other “sensors” which will run in parallel with the default search. From monitoring results of enhancements we’ve added to some of our other Entrez databases.

A number of these complaints are fair and we’ll be doing our best to address them. With the large number of users we have, it will be clear what areas we’ll improving and what areas will need more work.

Ik begin nu een beetje te begrijpen wat Graham hiermee bedoelde.

Want toevalligerwijs of niet, zijn er enkele zaken ingrijpend veranderd in PubMed, veranderingen die Anna’s eisen lijken in te willigen.

Welke veranderingen dat zijn en wat voor een effect ze sorteren wordt in deel 2 van deze trilogie besproken, zie HIER





Presentatie Geert van der Heijden op Slideshare

23 04 2008

De presentatie van Geert van der Heijden van vrijdag 18 april j.l. (BMI-ALV) is nu op Slideshare geplaatst (Lieuwe Kool slaat enkele web 2.0 stappen over ;).
Ik had er al in een eerder post melding van gemaakt, maar daar stond het zo temidden van de grote hoeveelheid tekst, dat het waarschijnlijk wat ondergesneeuwd raakte.
Verder had ik nog geen ervaring met embedden van een slide-serie, dus dat leek me meteen een aardig experimentje. Jammer dat Geert’s presentatie niet gepodcast is, want dan had ik meteen de SPOETNIK opdracht van deze week voltooid. Ik loop wat dat betreft nogal achter.

Fijn dat Geert deze presentatie ter beschikking heeft gesteld. Zo kan men even rustig Geerts’ verhaal in zijn eigen woorden (na) lezen in plaats van de afgeleiden hiervan op deze of gene blog.
Zo kan ik ook nog even lezen wat ik het eerste kwartier heb gemist.

Presentatie Geert staat online op slideshare: