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Searching Nutrition In PubMed & Embase: The Winner Is…

By Eric Rumsey and Janna Lawrence

As we’ve discussed, the big problem in searching for food-diet-nutrition subjects in PubMed is that the subjects are not together in a convenient bundle, as most subject groupings are in PubMed. To get a list of articles that includes food, diet and nutrition, it’s necessary to search each of these areas separately and then bundle them together into one search set.

When we first wrote about the difficulty of searching food-diet-nutrition in PubMed in 2013, we stated clearly that much of problem is caused by the fragmentation of the the relevant MeSH terms. So, jump forward a year. About two months ago, our library got institutional access to Embase.com, sometimes called the “European MEDLINE.” Embase includes all of the articles in MEDLINE, as well as many other articles, and uses its own subject heading system. Because we’ve long been aware that food-diet-nutrition subjects are generally given more attention in Europe than in the US, we thought that the subject might get better treatment in Embase than it does in PubMed. We weren’t disappointed…

A Nutrition explosion that includes Food and Diet

Embase uses explosions to bundle related subjects together, much like PubMed, and, as we were hoping, it does indeed bring food, diet and nutrition subjects together in a convenient bundle. This is a great advance over PubMed. It becomes easy to combine a subject of interest with food-diet-nutrition, in one simple step. For example, using Embase.com format:

‘Heart disease’/exp AND Nutrition/exp
Neoplasm/exp AND Nutrition/exp
‘Mental function’/exp AND Nutrition/exp

To do equivalent searches in PubMed, it’s necessary to do a hedge/filter search, such we have developed, or to search food-diet-nutrition terms separately and combine these with the subject of interest.

Better treatment of “Food” in Embase

Certainly having the inclusive food-diet-nutrition explosion is the biggest advantage in Embase. But there are other problems in PubMed, especially in the way the Food explosion is treated. In both Embase and PubMed, Food is the largest food-diet-nutrition explosion. There are several difficulties with this explosion in PubMed. An overall complication is that Food and Beverages have a confusing relationship. They are together in one explosion Food and beverages, which is made up of two separate explosions, one for each of the terms. If the user knows enough to search Food and beverages, he/she will get both terms. But if the user searches “Food,” the search will not include beverages. In Embase, beverage is an explosion that’s included in the food explosion, so searching “food” will retrieve articles on beverages.

Several other problems with the Food explosion in PubMed are caused by a lack of detail, in comparison with Embase. Some examples:

  • In PubMed, Fruit is not an explosion, although there are individual fruits included in the Plants explosion, usually under their Latin plant name. In Embase, the fruit explosion has 43 terms under it, 6 of which are themselves explosions.
  • In PubMed, Spices is an explosion with one term under it – Black Pepper. In Embase, the spice explosion has 31 terms listed under it, 4 of which are themselves explosions.
  • In PubMed, specific kinds of red meat, e.g. beef and pork, do not have their own MeSH terms; instead, they’re indexed under the general term Meat. In Embase, the meat explosion contains a red meat explosion, which has 7 terms, including beef and pork.

Related to the lack of detail in the Food explosion in PubMed is that many foods, especially plant-based foods, are not retrieved in a search for “food” because they don’t have specific terms in the Food explosion. We have written some “case studies” of this on chocolate, cranberries, and olive oil, all of which are in the Food explosion in Embase, but not in PubMed. We have also written an article on red meat being difficult to search in PubMed because there is no MeSH term for it; as mentioned above, Embase does have a term for it.

Looking at articles in Embase and PubMed that mention specific foods in the article abstract, it’s almost always the case that Embase’s detailed indexing will include descriptor terms for the specific foods, and PubMed usually will not. Comparison of article indexing is easy to do because Embase provides a “Source” filter, that makes it possible to limit to articles that are included in both Embase and PubMed. Each of the articles retrieved using this filter has a direct link to the citation in PubMed.

PubMed Advantages

The biggest advantage of PubMed, of course, is that it’s free to world. Embase, on the other hand, is an Elsevier product and is only available at institutions that have a subscription.

Another PubMed advantage is its simple Google-like interface, which is certainly more comfortable to most people. Embase uses an older style of interface that may appeal to librarians more than to most users. For anyone with a serious interest in food-diet-nutrition, though, we would say it’s definitely worth learning.

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New Resource: Board Vitals

Board Vitals Logo

Board Vitals is an exam preparation database. At this time, Hardin Library has subscribed to question banks for: Dermatology, Emergency Medicine, Neurology Shelf Exam, OB-GYN Shelf Exam, Otolaryngology, Pathology, Psychiatry, Psychiatry Vignettes, Child and Adolescent Psychiatry, and Radiology.

According to Board Vitals: “we provide up-to-date explanations from the literature with our answers and give you detailed feedback and assessment of your progress broken down by subject areas. With each question you can see how you compare to your peers, and gauge the difficulty of the question by what percentage of your peers answered it correctly or chose the same option you did.”

To use Board Vitals, you will need to:

  1. Access Board Vitals via the health science resources page: http://www.lib.uiowa.edu/hardin/eresources/
  2. Click the link that says “If Signing Up for the First Time, Click Here.”
  3. Fill out the form and you are ready to go.

You will have the option to select an area and then build a custom exam. The number of questions available is listed, and you can choose between a review or a timed exam. The reviewed exam provides explanations whether you answer the question or not. It will also show you how many exam takers correctly answered the question.

You can choose to answer between 1-50 questions, and, once you’ve used the resource, you’ll notice that you can choose to answer new questions, all questions, or incorrect questions.

Board Vitals is only one of the resources that Hardin Library provides for exam preparation. To find out about other resources, check out our Board Review Materials LibGuide http://guides.lib.uiowa.edu/boardreview

As always, please feel free to contact us http://www.lib.uiowa.edu/hardin/contact if you have any questions, comments, or concerns.

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New AccessMedicine App

AccessMedicine Screenshot

AccessMedicine is a collection of clinical tools and electronic textbooks. The app is powered by Unbound Medicine and provides access to a small portion of AccessMedicine.

The included resources are:

Quick Medical Dx and RX – Contains evidence-based outlines of conditions and disorders most often encountered in medical practice.

Fitzpatrick’s Color Atlas of Clinical Dermatology – This landmark digital reference facilitates visual diagnosis by providing color images of skin lesions, plus a summary outline of skin disorders and diseases.

Diagnosaurus – A differential diagnosis tool with more than 1,000 diagnoses. Browse by symptom, disease, or organ system.

Pocket Guide to Diagnostic Tests – This handy guide is a quick reference to the selection and interpretation of commonly used diagnostic tests include laboratory procedures in the clinical setting.

This app is available for Android and iOS (iPhone, iPad, iPod Touch) devices. In order to download and continue to access to the app, you must have an active AccessMedicine account and sign in every 90 days through Hardin Library.

 1.       Go to AccessMedicine via Hardin Library http://purl.lib.uiowa.edu/accessmed

2.       Create an account by clicking on the box at the top right of the screen that says “Univ of Iowa Hardin Library.”

3.       Select “Login or Create a Free Personal Account.”

4.       Once you have your username and login, download the app from Google Play or the iTunes App store.

5.       Login with your AccessMedicine Account.

As always, if you have questions, comments, or concerns, please do not hesitate to contact us.

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Plant-Based Foods – An Inclusive PubMed Search

By Eric Rumsey and Janna Lawrence

In our earlier article on searching for plant-based foods (PBF) in PubMed, we suggested that a quick way to search the subject is to combine MeSH plant-related explosions AND our Food-Diet-Nutrition (FDN) hedge. This works quite well, especially for citations after 2002. In that year the Plants explosion was greatly expanded by the addition of several hundred new MeSH plant names. Before that, articles on specific plants were indexed inconsistently. Sometimes they were put under the plant family name, in which case they were included in the Plants explosion, and in other cases they were indexed under other terms like Vegetables, Fruit, or Plants, Edible, that are not in the Plants explosion.

In order to do the most inclusive search for plant-based foods, including citations before 2002, we have created two hedges, to be used for all the years in PubMed. These hedges include other MeSH terms and text-words, to supplement the plant-related MeSH term search strategy that works well after 2002. We have done fairly thorough testing of the two hedges, and we recommend the first hedge for most searches. It uses MeSH terms, and it emphasizes precision, which means that it gets somewhat fewer citations, but the citations are more likely to be on target. For both of the hedges, we’ve combined them in an OR search with the “Plants AND FDN” hedge search mentioned above.

Here’s the first hedge – Recommended for most searches – Emphasizes Precision:

((Plants [mesh] OR Plant Preparations [mesh]) AND (food OR foods OR beverages OR diet OR dietary OR vitamin OR vitamins OR nutrition OR nutritional OR nutrition disorders OR food industry OR nutritional physiological phenomena OR dietary fats OR dietary proteins OR feeding behavior)) OR (Vegetables [mesh] OR Fruit [mesh] OR Cereals [mesh] OR Plants, Edible [mesh] OR Soybeans [mesh] OR Dietary Fiber [mesh] OR Flour [mesh] OR Bread [mesh] OR Diet, Vegetarian [mesh] OR Nuts [mesh] OR Condiments [mesh] OR Vegetable Proteins [mesh] OR Tea [mesh] OR Coffee [mesh] OR Wine [mesh])

[9.5.14. Hedge revised - Number of citations: 294,149]

To use this search, click this link. You can also copy the text above and paste it into the PubMed search box. If you have a personal “My NCBI” account in PubMed, the hedge search can be saved for later use, or it can be made into a search filter. For information on setting up and using saved searches, see here; for more information on filters, see here. Commentary on terms in this hedge (If the “Year introduced” is not given, the term has been in MeSH since its launch in 1966):

  • Vegetables [mesh] Citations: 84411 An explosion that includes about 25 specific vegetables, including Onions, Soybeans, Daucus carota, and Solanum tuberosum. This is a relatively small proportion of all vegetables, which are indexed with their species or family name, in the Plants explosion.
  • Fruit [mesh] Citations: 57179 Notably, this is NOT an explosion. All particular fruit types are indexed with their species or family name, in the Plants explosion.
  • Cereals [mesh] Citations: 73516 An explosion that includes 8 cereals, including Avena sativa, Triticum and Zea mays. This is an important group, since it includes the world’s staple foods–wheat, rice, and corn.
  • Plants, Edible [mesh] Citations: 38945 An explosion that includes several terms elsewhere in this hedge that get more citations when they’re searched separately. The term Plants, Edible by itself gets 5402 citations.
  • Soybeans [mesh] Year introduced: 1986 Citations: 19284 An explosion that includes Soy Foods, Soy Milk, and Soybean Proteins.
  • Dietary Fiber [mesh] Year introduced: 1982(1977) Citations: 13468
  • Flour [mesh] Citations: 3570
  • Bread [mesh] Citations: 3115
  • Diet, Vegetarian [mesh] Year introduced: 2003(1963) Citations: 2537
  • Nuts [mesh] Citations: 2074
  • Condiments [mesh] Citations: 1945 An explosion that includes Spices.
  • Vegetable Proteins [mesh] Year introduced: 1975 Citations: 1515
  • Tea [mesh] Citations: 6904
  • Coffee [mesh] Citations: 4751
  • Wine [mesh] Citations: 7256

Here’s the second hedge - Emphasizes Recall:

((Plants [mesh] OR Plant Preparations [mesh]) AND (food OR foods OR beverages OR diet OR dietary OR vitamin OR vitamins OR nutrition OR nutritional OR nutrition disorders OR food industry OR nutritional physiological phenomena OR dietary fats OR dietary proteins OR feeding behavior)) OR (vegetable OR vegetables OR fruit OR fruits OR cereal OR cereals OR spices OR condiments OR flour OR nut OR nuts OR vegetarian OR soy OR soybean OR soybeans OR bread OR Tea OR Coffee OR Wine)

[9.5.14. Hedge revised - Number of citations: 380,361]

To use this search, click this link, or see instructions above with first hedge. Most of the words used in this hedge are text-word versions of the MeSH terms used in the first hedge. Since this emphasizes “recall” instead of “precision,” it gets more citations than the first hedge. But the citations are less likely to be relevant. We looked closely at citations using the two hedges, and it was easy to see the lesser relevancy of the citations in the second hedge. Most of these, of course, are retrieved because they mention words that are in the abstract (e.g. fruit, vegetables) but which are not assigned as MeSH terms.

A word about searching for older citations

When we first realized that most of the plant name MeSH terms were only introduced in 2002, it seemed like a serious problem. However, as we’ve looked back retrospectively, we’ve come to see that there really wasn’t much research attention given to the subject in the earlier days of MEDLINE, especially before about 1990.

We’ve done detailed work to study this, but in this article we’ll just give a couple of anecdotal examples of what we’ve found. We looked at the number of citations that contain the word “fruit” since 1968, and found that this stayed flat, at about 400 mentions per year, until about 1990. It’s grown fast since then, and in 2013, the word is in about 8000 citations. In another example, we found that there are 70 articles in all of PubMed that have “sweet potato” in the title, and are on human subjects. All but three of these are after 1992; zero citations from 1980-1992 contain the words in the title. So, if it seems like the hedges in this article aren’t finding many citations before 1990, it’s probably because there just aren’t many to be found.

Things improve in the 1990s. It appears, from our retrospective examination of citations on FDN, that as the volume of research on the subject increased, NLM gradually improved the quality of MeSH indexing to accommodate it. The coverage of more prominent plant families improved, and the application of existing FDN MeSH terms became more consistent. So in the 1990s, even before the mass introduction of new MeSH plant terms in 2002, FDN indexing and retrieval was improving.

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PubMed Food Problem: Red Meat

By Eric Rumsey and Janna Lawrence

As we’ve discussed before, searching for “red meat” in PubMed is difficult because the subject is poorly covered in the MeSH vocabulary. Not only is there not a term for “red meat,” but there are also no MeSH terms for specific kinds of red meat (beef, pork, etc.). There is only the one all-inclusive MeSH term Meat, which includes all kinds of meat, as well as fish and poultry. So this is a rare case in PubMed in which MeSH is essentially useless. The only way to do a thorough search is to use text words that include the phrase “red meat” or that contain specific types of red meat – e.g. “red meat” OR beef OR pork… [etc]

The problem of searching for red meat has returned to our attention recently for two reasons. One is that the subject continues to be in the news. Last winter when we wrote, it was getting attention because red-meat nutrient carnitine was reported to be linked to heart disease. And recently red meat has been back in the news because it’s been reported that tick bites can trigger an allergy to red meats.

The other reason we’ve been thinking about red meat is that, coincidentally, our library has recently gotten a subscription to Embase, which a European-based medical literature database that’s the main alternative to PubMed/MEDLINE. Of course, the first thing we did with new access was to compare the indexing, and especially the explosions, in Embase to PubMed for Food-Diet-Nutrition subjects. We have found several cases in which Embase is better. One case in which it’s clearly better is red meat.

Here’s the explosion in Embase:

Red Meat
… Beef
… Lamb Meat
… Mutton
… Pork
… Rabbit Meat
… Veal
… Venison

This can’t be used directly in PubMed, of course, but at least it gives an idea of specific meats to search in PubMed as text words.

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MeSH on Demand Tool Launched

The National Library of Medicine (NLM) recently launched a new tool called MeSH on Demand. Now, you can find MeSH terms from text you input! MeSH on Demand is available online in the MeSH Browser.

First, input up to 10,000 characters of text into MeSH on Demand, your text will be processed using the NLM Medical Text Indexer (MTI) program.

mesh on demand example

Then, MeSH on Demand will identify MeSH Headings, Publication Types, and Supplementary Concepts from your text.  Qualifiers (subheadings) are not identified.

mesh on demand results

Disclaimer:
MeSH terms are machine-generated without human review.  Results will be different from human-generated indexing.

Comments:
The  NLM welcomes questions and comments about MeSH on Demand.  Fill out a contact form:  http://apps2.nlm.nih.gov/mainweb/siebel/nlm/index.cfm/

 

 

 

 

 

 

 

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Twitter Redesigns Profiles, Adds Engagement Features

Twitter has rolled out a new design for users’ profile pages. The important changes are:

  • Graphics are emphasized, with much larger header images
  • Tweets that are getting more “engagement” are in a larger font. The algorithm used to measure a tweet’s engagement is not revealed by Twitter. Engagement appears to be based on the number of retweets, mentions, favorites, and clicks. Apparently large-font tweets only happen on desktop Twitter, not on mobile.
  • You can choose a tweet that you want to emphasize, to be “pinned” to the top of your profile.

More information from Twitter on the new profile:
Coming soon: a whole new you, in your Twitter profile

To see further commentary, search for appropriate terms in Google and Twitter. For instance:

To keep up with what’s happening at Univ Iowa and with Twitter, follow @HardinLib and @EricRumsey.

 

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PubMed Food Problem – Olive Oil

By Eric Rumsey and Janna Lawrence

[Check out additional articles on PubMed & Plant-Based Foods]

Olive oil as a healthy food is a highly popular topic among consumers. It’s also popular among researchers, as shown in a list of the 100 most popular research articles of 2013, by the Altmetric site, in which the number two ranking article is a comparison of olive oil and nuts for prevention of cardiovascular disease, in NEJM. This article shows a major problem with the MeSH indexing of olive oil. Even with its trending popularity, olive oil does not have its own MeSH term. In the NEJM article, and in most articles on olive oil, the only MeSH term that corresponds with olive oil is Plant Oils. This is a problem because Plant Oils is not in any food-diet-nutrition (FDN) explosion, and is therefore not retrieved by broad searches for FDN.

Fortunately, articles on olive oil are often picked up by broad FDN searches because they have other FDN-related MeSH terms. But in many cases, they are not. Here are some examples of articles with olive oil in the title that are not retrieved using our broad FDN hedge because they contain no MeSH terms or text words relating to food, diet or nutrition:

As we’ve discussed previously, most plant-based foods are difficult to search in PubMed because the MeSH terms for them are in the Plants explosion and not in any FDN explosion. Notably, olive oil has a different problem. It’s like other plant-based foods in not having a MeSH term that’s in an FDN explosion. But instead of being in Plants, its MeSH term, Plant Oils, is in the Chemicals and Drugs explosion.

There’s another baffling quirk in the MeSH indexing of olive oil. Although it doesn’t have its own MeSH term, there are MeSH terms for several other dietary oils in the Dietary Fats explosion (which, of course, is retrieved in broad FDN searches). These other dietary fats are Cod Liver Oil, Corn Oil, Cottonseed Oil, Safflower Oil, Sesame Oil, and Soybean Oil. None of these has close to the number of citations as there are on olive oil. We searched for each of these oils as a phrase in the title, and compared this with olive oil, and found, remarkably, that the total of all of these other oils combined is about the same as the number for olive oil by itself. So why is there not a MeSH term for olive oil?!

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PubMed Food Problem: Cranberry & Cranberries

By Eric Rumsey and Janna Lawrence

[Check out additional articles on PubMed & Plant-Based Foods]

Part of the problem in searching for food in PubMed is that it’s often the case that there’s a fuzzy border between between food and medicine.   A food that is enjoyed for its taste and general nutritional benefits may have properties that make it therapeutic for specific health conditions. A good example of this is cranberries, and cranberry juice, which may have benefits for prevention of urinary tract infections.

As with most plant-based foods, in MeSH indexing, cranberry is in the Plants explosion, and it’s not in any food, diet or nutrition (FDN) explosion. Fortunately, most articles on cranberries and cranberry juice are assigned some FDN indexing terms so that they are retrieved in broad FDN searches. Articles on cranberry juice are often under Beverages, and some articles on cranberries are under Fruit or Dietary supplements.

To show how this works in PubMed, we searched for cranberry or cranberries in the article title, limited to human, and retrieved 322 articles. We then combined this with our broad hedge search to get all articles that contain food, diet or nutrition MeSH terms or text words. This retrieved 255 articles — 79% of the cranberry/cranberries articles, which is a fairly good retrieval. But still, it’s certainly notable that there are 67 articles that are not retrieved, many of which appear to be very much on target, that don’t contain any FDN MeSH terms or text words. Here are some examples:

As we mentioned above, plant-based foods are tricky to search in PubMed because the name of the food plant is usually only in Plants, and not in any FDN explosion. The five articles above are all indexed under Vaccinium macrocarpon, the taxonomic name of cranberry, which is in the Plants explosion. So if you were searching for articles on urinary tract infections and plant-based foods, a strategy that would retrieve these articles would be to combine Urinary Tract Infections AND Plants.

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Is Chocolate A Food? A Problem In PubMed

By Eric Rumsey and Janna Lawrence

[Check out additional articles on PubMed & Plant-Based Foods]

As we’ve written, searching for food-related subjects in PubMed is difficult because of the way the MeSH system is organized. Plant-based foods are especially difficult because in most cases they are treated mainly as plants rather than food.

One result of treating plant-based foods as plants is that the MeSH term is usually the botanical plant name; in the case of chocolate it’s Cacao. This is usually not a serious problem when searching for specific substances because the common food name maps to the botanical MeSH term.

A more serious consequence of treating plant-based foods as plants instead of foods is that they are usually not in any food-related explosion, but only in the Plants explosion. So the only occurrence of chocolate (Cacao) is here:

Plants
   Angiosperms
      Sterculiaceae
         Cacao

The reason this is a problem is because articles on chocolate/Cacao will not be retrieved in a search for Food. So, for example, if you do a general search for food-related causes of migraine, you will not retrieve this article:

Chocolate is a migraine-provoking agent
Journal: Cephalalgia
http://www.ncbi.nlm.nih.gov/pubmed/1860135

This is not retrieved by searching for food because Cacao is not in the Food explosion. More broadly, however, it’s also not retrieved with the comprehensive Food-Diet-Nutrition hedge that we’ve discussed previously, which includes text-words as well MeSH terms and explosions.

Here are several other articles on health and medical aspects of chocolate that are not retrieved by our broad Food-Diet-Nutrition hedge:

If chocolate were the only case of a plant-based food that is not retrieved in a broad PubMed search for food-related topics, it would be a trivial matter. But that’s far from being the case. There are many plant-based foods that have the same problem in PubMed. We will be writing on several of these foods in the next few months, so keep an eye on this space!