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Nursing and Allied Health: Gestational Diabetes and Fast Foods (MeSH)

Online Learning

Gestational Diabetes and Fast Foods

mesh

PubMed Search Using Mesh Terms

 

Gestational Diabetes and Pregnancy

 

From the MeSH Browser

 

Gestational Diabetes refers to DIABETES MELLITUS, not DIABETES INSIPIDUS; do not confuse with PREGNANCY IN DIABETICS where a diabetic becomes pregnant

Scope Note
Diabetes mellitus induced by PREGNANCY but resolved at the end of pregnancy. It does not include previously diagnosed diabetics who become pregnant (PREGNANCY IN DIABETICS). Gestational diabetes usually develops in late pregnancy when insulin antagonistic hormones peaks leading to INSULIN RESISTANCE; GLUCOSE INTOLERANCE; and HYPERGLYCEMIA.
 
If you are researching the topic, of patients with GESTATIONAL DIABETES, your PICO statement is looking at what patient population?. 
 
You might further narrow your research by combining the term gestational diabetes with one of 3 subjects you found in the MeSH browser:   INSULIN RESISTANCE; GLUCOSE INTOLERANCE; and HYPERGLYCEMIA.
 
How do you translate the PICO statement into a research strategy?
 
Problem or Patient Population
  • Gestational Diabetes
Intervention
  • Dietary Intervention
  • Lifestyle Interventions
Comparison
  • Intervention versus non-intervention
Outcome
Okay, let's translate this into a "research strategy"

 

Open PubMed.  We are looking for the intersection of Gestational Diabetes and Diet; We are also looking for the intersection of Gestational Diabetes AND Lifestyle:

  •  How does (diet OR lifestyle) affect (insulin resistance) in (gestational diabetes)?
  •  How does (diet OR lifestyle) affect (glucose intolerance) in (gestational diabetes)?
  •  How does (diet OR lifestyle) affect (hyperglycemia) in (gestational diabetes)?
  •  What articles, if any, describe educating the patient (patient education) on each of these facets?

 

But first, let's discuss the image below:  What are Boolean Operators?

 

 

  • AND finds articles where both terms are mentioned
  • OR finds articles where either term is mentioned
  • NOT:  A NOT B finds articles where one term is mentioned, but not the other term.

 

A BOOLEAN SEARCH OR NATURAL LANGUAGE PROCESSING?

 

The most common types of searches are:

  1. Boolean Search Query: PubMed Advanced Search  (Gestational Diabetes) AND (Lifestyle OR Diet)
  2. Natural Language Processing (NLP) Query- Google uses both algorithyms and relevancy ranking to find articles based on the key terms you have entered.  When you enter more than one keyword, results that include all terms are ranked at the top of the list.  This is called relevancy ranking.  The most relevant articles are ranked at the top of the list.
  3. Artificial Intelligence or Machine Language Applications:  Today, a lot of hype still surrounds AI development, which is expected of any new emerging technology in the market. An application such as Artificial Intelligence (AI)I or Machine Language (ML) is applied to a database or a group of results. PubMed uses machine language and algorithyms to find similar articles that share keywords in the Title and Abstract of articles. Using AI, you might enter a Medical Subject Heading or a term (MeSH). The computer look for similar terms that are closely related to your term.  A good example is the PubMed "Find Similar Articles".  PubMed returns articles matching your search query.  When you examine a particular article,  the section "Similar Articles" are not based upon your search terms.  The computer program looks at the common key words in the abstract and title of a record. It then finds articles that have the same words in the title and abstract.  Often, your search results resulting from your query don't include these articles. They are linked below the abstract in an area called "Similar Articles" This is a great PubMed feature. However, it is important to remember that filters applied to your search query are not applied to "Similar Articles).

 

 

3.Information Visualization (INFOVIS 2001).  Uses  algorithyms and geospatial word clouds that extracts key words and topic models). Information Visualization is a specific type of Artificial Intelligence (AI). AI tools such as INFOVIS have been popular since 1998. The image below helps you visualize how a specific algorithym like "PubMed's Similar Articles" might work. Each cloud is composed of articles that have many similar terms.  When you move further from the center of the cloud, the terms in common become more distant. There is evidence that AI was present much earlier. Decades before this definition, the birth of the artificial intelligence conversation was denoted by Alan Turing's seminal work, "Computing Machinery and Intelligence" (PDF, 89.8 KB), which was published in 1950.

University of New Hampshire Scholars Repository, Franklin-Pierce School of Law (Fall 1-1-2012). Patent Landscape of Influenza A Virus Prophylactic Vaccines and Related Technologies. Patent literature is grouped by an algorithym (s) and machine learning software.

 

Libraries are constantly adapting to new technology.  Look to the future!

Boolean Advanced Search in PubMed

The Boolean Advanced Search permits you to combine terms with AND, OR and NOT.

Citation Mapping in PubMed

 

PubMed Forward Citation Mapping

 

PubMed contains a feature called "Forward Citation Mapping". You find this in the "Bibliographic Record"  below "Find Similar Articles"  You can find additional information on your topic by looking at the forward citations. We found 6 articles on Gestational Diabetes and Fast Foods published since 2016. We find more current information citing back to our article through Citation Mapping.

 

The words, "Cited by" appear below "Similar Articles"

 

 

Image of Citation Mapping

 

This is a citation map representation:  "Forward Citation Mapping"