PRINCIPALES PRINCIPIOS Y PRÁCTICAS CONTABLES

Bibliographic databases are tools for finding published materials; for example, library catalogs are bibliographic databases used to find items owned by a library. Each record describes a specific work, such as a book or journal; fields include title, abstract, author, author affiliation, publication date, volume, page numbers, subject headings, etc. Chapter 5 describes a variety of bibliographic databases and how to build effective search strategies for them. This section focuses on one aspect of database searching: the use of controlled vocabularies. It is important to understand how to use these vocabularies because they can make information retrieval more efficient. They are also extremely important in the devel- opment of pharmacy information systems. Although perhaps not consciously aware of it, pharmacists see and use controlled vocabularies in their daily practice whenever they use a bibliographic database or pharmacy information system.

Every field in a bibliographic database is associated with a list (index) of all the data or words in each field. Most electronic databases today have the capability to search all fields for keywords; for example, a search for the term “atorvastatin” in all database fields would retrieve records with that term appearing in any field (e.g., title, abstract, or subject). But many of the documents described in those records would not truly reflect the subject of atorvastatin. For example, an article about hypertension might incidentally mention in its abstract that atorvastatin was prescribed for the patient. A search of all fields for the term atorvastatin would retrieve this record, but the article would not be relevant because it focused on a condition unrelated to the drug of interest.

Users can restrict searches to find their keywords in the index of one particular field (see Chapter 5). It is relatively straightforward to search for information in fields that are well defined, such as author and title; however, fields describing the subjects of articles are more ambiguous because subjects can be described in many ways. As discussed in Section 4.4.2 using the example of myocardial infarction, if a database uses a specific term to describe a subject, then a search of the subject field must use the same term that the database has in the subject field. A search for “atorvastatin” restricted to the subject field would retrieve records that listed atorvastatin only in the subject field and eliminate articles in which it was only briefly mentioned, but was not the subject of the article. This search would yield better results than a search of all fields.

However, the success of the search would depend on what term the database used. If the database used “Lipitor” rather than “atorvastatin,” the subject search would be unsuccess- ful. The user would be required to enter synonyms for atorvastatin in the search query in

order to locate all records on the subject. When databases employ controlled vocabular- ies, such issues can be avoided. This section describes how searchers can use controlled vocabularies to focus subject searches and retrieve better results.

4.5.2 Term Mapping

A bibliographic database’s controlled vocabulary is a set of standard terms, or descriptors, used to describe subjects. A controlled vocabulary might designate the term “aspirin” as synonymous with the trade name “Ecotrin” and the chemical name “acetylsalicylic acid.” Database designers choose preferred terms as descriptors of subjects (e.g., aspirin) and identify synonymous terms that database users might enter in searching for that subject (entry terms). The process by which a database bundles entry terms and points them to preferred terms is called “term mapping.” A search of the term “Ecotrin” or “acetylsalicylic acid” in a subject field that used this controlled vocabulary would find records containing “aspirin” in the subject field because the entry terms mapped to aspirin.

Humans use a variety of names, synonyms, abbreviations, acronyms, etc. to describe a single topic. Computers can retrieve records with entry terms in any field, but they require a controlled vocabulary to map the entry terms to the preferred subject terms. For example, as discussed before, a database might use “myocardial infarction” as the preferred term for “heart attack.” “Heart attack” is designated as an entry term. A user who did not know the preferred term and limited the search for “heart attack” to the subject field would rely on term mapping to find records with “myocardial infarction” in the subject field. If the entry term were not included in the controlled vocabulary as a synonymous term, it would not map to the preferred subject term and the search would retrieve no results.

Limiting bibliographic database searches to the subject field helps focus searches by exclud- ing records that contain entry terms in a field other than the subject field. In order to limit a search to the subject field of a database effectively, the searcher must use the same terminol- ogy as the database in defining the subject; however, it is often difficult to guess the correct database terminology. Some interfaces to bibliographic databases provide messages to users that their entry terms do or do not map to subject headings (e.g., “Did you mean…?”).

Some databases include a thesaurus (structured list) of subject headings that users can search to find the preferred terminology and effectively search the subject field using the appropriate vocabulary. Other databases that use a controlled vocabulary do not include a thesaurus. In these cases, the subject search becomes an iterative process in which the user first searches all fields, finds a few relevant results, identifies index terms associated with those records, and launches new searches for those terms in the subject field.

Some databases have no controlled vocabulary, so there is no facilitated subject search capability; the database searches every field for the terms exactly as entered. This is called “free text” searching. Humans understand synonomy (many terms with the same mean- ing) and ambiguity (many meanings of one term) in language. We intuitively include syn- onyms in describing a term, and we understand the meaning of terms by their context. A database without a controlled vocabulary cannot manage synonymy or ambiguity; free- text searches should include synonyms, and database search tools should be used to define terms as precisely as possible. Chapter 5 discusses database search tools.

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Controlled vocabularies help manage synonomy through term mapping, decreasing the need for users to include all synonyms in subject searches. However, remember that good term mapping is dependent on the database designers’ inclusion of all synonyms in the vocabulary. Also, synonomy of terms depends on the context of the database. A biblio- graphic database might define the generic and trade names of drugs as synonymous for the purpose of defining subjects of articles, but a formulary database of drug products might not because brand-name drugs and generic drugs are different products, possibly with dif- ferent formulations and prices.

Controlled vocabularies also help manage ambiguity. For example, the term “bridge” could be used to describe a piece of dental work, a structure that helps people cross a river, or a card game. The biomedical literature database, MEDLINE, uses the preferred term, “denture, partial, fixed,” to describe the concept of dental bridges, and it includes the term “bridge” as an entry (i.e., synonymous) term. A search of the subject field for the term “bridge” would retrieve records containing “denture, partial, fixed” as a subject and exclude the large number of records that contain the word “bridge” in an entirely different context. For example, it is likely that a MEDLINE search of all fields for “bridge” would retrieve a large number of records that refer to molecular bridges or records that contain the phrase, “bridge the gap” in the title or abstract. Using the controlled vocabulary helps ensure that the searcher uses the same language the database uses to describe the subject. 4.5.3 Medical Subject Headings

In the biomedical literature database, MEDLINE, the preferred controlled vocabulary terms of the subject field are called medical subject headings (MeSH). MEDLINE includes a thesaurus, the MeSH database, to help users locate preferred MeSH terms for their sub- jects. MeSH evolved over 150 years.1 _{Long before computers and electronic databases, con-}

trolled vocabularies were used to search the biomedical literature.

The idea that every article in the medical literature should be tagged with subject head- ings and indexed under those subjects was conceived in the late nineteenth century by John Shaw Billings, assistant to the U.S. Surgeon General, who collected and stored the medical literature of the time. In 1874, he began indexing the collection by author and sub- ject and in 1880 produced the “Index-Catalogue of the Library of the Surgeon-General.” Because of the long delay from publication of an article to the article being included in the index catalogue, Billings started publishing monthly current awareness updates of the index, called Index Medicus.

Billings and his successors continued the work of indexing medical literature until 1956, when Congress named this collection the National Library of Medicine (NLM) and made it part of the U.S. Public Health Service (later the National Institutes of Health). In 1960, the NLM produced a list of standardized subject headings, the first edition of MeSH. The only way to search the biomedical literature by subject at that time was by using MeSH. Users looked for entry terms in print thesauruses to find appropriate MeSH terms to search subject indexes.

The first computer-searchable database of medical literature, called the Medical Literature Analysis and Retrieval System (MEDLARS), was produced in 1964. Part of this

system was the MeSH database, which was the first version of MeSH to be organized in a hierarchy with “broader than” and “narrower than” relationships. At that time, searches could be submitted to the NLM, where they were formulated and entered into a computer via punched paper cards. Turnaround time for a search request was 4–6 weeks! In 1971, NLM introduced MEDLARS Online (MEDLINE), which could be searched via telecom- munications networks. Users still were required to use MeSH terms to search the subject field. MEDLINE evolved with computer technology; it has been offered in a variety of for- mats such as CD-ROMs and stand-alone database packages.

As the Internet developed, searching became more user friendly, databases became accessible online, and free-text searching became possible. Searchers can now find their keywords in any field of MEDLINE. Free-text searching is very powerful; however, using the MeSH controlled vocabulary helps to focus searches in bibliographic databases and retrieve more relevant results. Chapters 5 and 11 discuss free-text searching in the Web environment. In 1997, the NLM produced PubMed, the first free, online version of MEDLINE. Currently, over 18 million records, each corresponding to a specific article, are contained in PubMed, and MeSH has over 25,000 subject headings. In 2009, approximately 50,000 new research articles were indexed and placed into PubMed each month.

4.5.4 Components and Organization of the MEDLInE Controlled Vocabulary

The main components of the MEDLINE controlled vocabulary are the MeSH terms. NLM designates MeSH terms as preferred terms to describe concepts. The MeSH terms have associated entry terms that can be mapped to MeSH terms. MeSH also includes qualifiers, which are subheadings that can be attached to MeSH terms. These are specific, standard- ized terms. Approximately 80 subheadings are available and no single MeSH term can be associated with all 80 subheadings. Subheadings are very useful in more precisely defining search terms and thereby focusing a search. For example, a MeSH term describing a dis- ease or condition can be associated with subheadings such as “drug therapy” and a MeSH term describing a drug can be associated with subheadings such as “therapeutic use.”

In addition to the two just mentioned, several subheadings are particularly useful to pharmacists. For diseases and conditions, “chemically induced” is a powerful subhead- ing to use when searching for a disease or condition that resulted from the use of a drug. For drugs, the many useful subheadings include “administration and dosage,” “adverse effects,” “classification,” “contraindications,” “pharmacokinetics,” “pharmacology,” and more. Searchers always have the option to include more than one concept in their search query so that a drug name and the term “adverse effects” could be searched together (“ANDed”) to retrieve all the records containing both terms. However, using the attached subheading ensures that the term will be associated with the main subject heading and not used in another unrelated context.

A third component of the MEDLINE controlled vocabulary is the substance index. Separate from the MeSH index, it was developed because of the need to index many new drug names. The drug names are “supplementary concepts” analogous to MeSH terms. Older drug names usually appear as MeSH terms because they were indexed before the supplementary concepts were implemented; newer drug names are usually indexed only as

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supplementary concepts. Entry terms will map to both the MeSH and substance indexes, but supplementary concepts do not currently have subheadings associated with them. When an individual searches a drug name that is indexed as a supplementary concept, the searcher must enter the subheading as a separate term.

The MeSH controlled vocabulary is a taxonomy (see Section 4.7) in which terms are organized in a hierarchy or “tree” structure. Each term in the taxonomy is related to other terms as either broader or narrower. Users can browse for terms in the MeSH database and see the hierarchical arrangement with broader and narrower terms.

Figure 4.1 shows an example of a tree structure in MeSH. The MeSH term for drugs is “pharmaceutical preparations,” and narrower terms fall below this term. The hierarchy conveys meaning and context. For example, gels are a type of colloid, which is a dosage form. MeSH is polyhierarchical, meaning that a subject heading (MeSH term) can appear in more than one category (hierarchy) because it logically falls into more than one cat- egory. For example, “colloids” falls under “dosage forms” in the “chemicals and drugs” branch of the tree, as well as under “chemistry, physical” in the physical sciences branch because not all colloids are drugs.

There are other relationships between MeSH terms that are associative. For example, the terms “anticarcinogenic agents” and “antineoplastic agents” are associated, but not exactly synonymous; therefore, each is a unique concept (i.e., MeSH term), rather than one being a preferred term and the other being a synonym. With these associative relationships, users obtain cross-references in the MeSH database. A search for “antineoplastic agents” shows the position of the term in the hierarchy, and it provides the cross-reference “see also anti- carcinogenic agents.” Some vocabularies have additional types of relationships between concepts so that they form an ontology (see Section 4.8).

Drug Combinations

Delayed Action Preparations Drugs, Investigational Pharmaceutical Preparations Dosage Forms Colloids Aerosols Emulsions Gels Designer Drugs Capsules

4.6 DRuG nOMEnCLATuRE STAnDARDS