As shown in Figure 1, Metta is constructed in terms of a front-end that serves as the web-based query user interface, and that interacts with a back-end that connects to the search engines to retrieve records from the 5 bibliographic databases.
In general, our aim was to simplify the process of submitting queries so that users did not need to concern themselves with complex queries, or the use of search tags or other specialized commands. On the other hand, we wanted to retain flexibility as much as possible, so that users could adjust the pre-set options for each database if they so desired. As shown in Figure 2, the Metta homepage represents a concise interface that achieved both of these aims. Users choose one of 4 different search tracks (indicated at left) to carry out either a search for human-related studies (i.e. articles which are indexed under Humans in each database); a search for case reports and similar articles; a search for systematic reviews and meta-analyses; or a general search (i.e. utilizing only the default settings of each search engine). Each search track has built-in settings designed to optimize comprehensive retrieval of the relevant types of articles, while minimizing irrelevant articles. The tailored search strategies underlying these settings are listed within the Help page (Additional file 1). Although users cannot alter the search track settings directly, this does not limit the scope of possible searches since queries can still be built up freely on the general search track. Furthermore, by clicking on “show options” for any search engine, users can manually over-ride or add options and restrictions at will, though in practice, few, if any users chose to manually adjust the search settings within individual search engine options.
In general, Metta employed the default query expansion and processing strategies of each database, with some exceptions (e.g. full-text searching was turned off for PsycINFO). Because all five databases employed three common search tags (title [ti], author [au] and abstract [ab]) these were permitted in user-entered queries. The permitted tags are shown as examples on the right side of the homepage, and in detail on the Help page (Additional file 1).
After a user inputs a query on the human track (e.g., olanzapine AND schizophrenia AND relapse) and clicks “search”, he or she will be directed to the search result page shown in Figure 3. The user inputted query and selected search track are indicated within the light yellow panel shown at the top. The query result summary is shown in the middle table, with columns showing each database with its number of results returned, connection status, and other parameter and status columns. The database name column offers the link to search result page (shown in Figure 3) of individual databases. Note that users may always switch between the result page and individual database result pages using the blue panel located at the top of the page. Figure 4 shows one of the individual database result pages, i.e. for PubMed. The snippet of each result record is in a Google-like format, showing the article title in a hyperlink to the article’s original link in the PubMed (or other source database) website, followed by journal title, publication year, issue & pages; then the authors & their affiliations; and finally abstract and original URL. Twenty records are displayed on a page.
On the result page (Figure 3), when the user clicks the “Export (with Deduplication)” link, this triggers Metta to pull all retrieved records from all databases, perform de-duplication for records from different databases (see below), and offer users the option to download the de-duplicated records as text files for offline use (Figures 5 and 6). The amount of time for export to be completed varies according to the number of records and their allocation among databases. Retrieval of full records was slowest for the PsycINFO database in which each record had to be re-queried and downloaded individually; although a thousand records could be exported in about a minute from the other databases, PsycINFO was at least 10 times slower. Users are offered two file download links for all the de-duplicated results, one for export in XML format (ideal for further computer processing) or BibTex format (this is designed to be compatible with with a wide range of commercial and open source reference manager software) (Figure 6).
Design and user issues
The front-end of Metta was intended to satisfy a wide range of different types of users, as well as a wide range of different search strategies. Certainly Metta was aligned to the needs of the majority of people searching the biomedical literature, who tend to carry out only one or two queries at a time, employ only one or a few search terms, and do not routinely use search tags [7, 8]. To accommodate the needs of users who employ an iterative approach (in which initial results are examined, and the initial query modified and resubmitted), we cached queries so that all previous queries from the same session were visible as users began to type in the query box.
A much more difficult decision was how to reconcile a unified query interface with the prevailing practice of many systematic reviewers to carry out long, complex queries that involve search tags and advanced commands which are specific to individual databases. The individual databases all have optional advanced search interfaces that allow users to build up queries consisting of separate query terms (linked to field tags) concatenated with AND, OR or NOT as well as restricting search term to specific text and meta-data fields. However, entering a search tag into Metta that is inappropriate for one or more of the databases will result in query errors. We did initially implement an Advanced Search page in Metta (patterned after PubMed’s Advanced Search Builder), but finally decided to remove it and only the basic query interface is currently active.
This decision was reinforced by an analysis of the reasons that reviewers exclude initially-retrieved clinical trial articles from final inclusion in systematic reviews (discussed in detail in [9, 10]). Briefly, we found that reviewers did not trust that metadata indexing of articles was adequately reliable, particularly with regard to study design aspects (such as randomization or use of placebos), and so a) did not employ these restrictions while carrying out initial searches, and b) utilized many different word and phrase combinations in the query, in an attempt to capture all possible relevant articles. This results in an initial search that may retrieve 10–100 times more records than are finally deemed to be relevant for inclusion in the systematic review.
The resources required to conduct systematic reviews are too great for several reasons. Construction of the query is a complex process carried out by specialists, typical searches return too many articles that will be excluded from the systematic review, and doing a comprehensive search across multiple databases is time consuming and incurs a lot of manual work in deduplication and other tasks necessary to clean up the search results. Metta is only the first step in a pipeline project that creates a series of computer-assisted tools to assist systematic reviewers  (another step of which is to re-tag clinical trial articles with study design labels). The pipeline is intended to present an alternate manner in which systematic review literature search and initial review could be performed which reduces resource utilization in several time and resource consuming areas. We felt that it was more important to keep Metta simple and predictable in its output, rather than designing it to handle extremely large sets of retrieved records (~5,000 or more) or to facilitate the entry of extremely complex queries by users via an advanced query builder.
Thus, for systematic reviewers to be expected to adopt Metta on a routine basis, it will be necessary to re-engineer multiple steps in the process by which articles are queried, retrieved, filtered and examined for inclusion. For example, better study design annotation and filtering of articles retrieved by Metta searches should give overall high-recall retrieval with better precision than is currently obtained by conventional searches, and thus largely obviate the need of reviewers to formulate highly complex queries. In any case, one of us (NRS) has found that it is easier simply to build up complex queries on a blank Word document and then cut-and-paste it into the Metta query window, than to build up queries step by step within an advanced query page.
The back-end of Metta is concerned with validating permissions for each search engine; submitting queries to each search engine; ensuring that an effective connection is established and that a meaningful result is returned in real time, and presenting the results for display by the web interface (front-end), with warnings given if one or more of the data sources failed to respond. In addition, the back-end is responsible for retrieving all records regardless of the number of records displayed on each result page or other limitations of size imposed by individual search engines, and exporting retrieved records. Each of these has special requirements and will be discussed in turn.
The login is currently implemented in two ways: For EMBASE, it uses explicit username and password stored in the configuration file; for Cochrane, CINAHL and psycINFO, it uses the university library’s authorization to login. When it is a production system, Metta will maintain a database of users’ access rights to the search engines; every user will only be allowed to query the search engines for which they have access rights.
Each query submitted to the query interface of Metta for a selected track is mapped to a query to each of the five search engines. The mapped query for a search engine needs to satisfy the constraints of the search engine so that it can be correctly processed by the search engine. The mapping is achieved via a mapping table that keeps track of the search field correspondences between the query interface of Metta for the selected track and the query interface of each search engine, and also via information about the pre-set query options for both query interfaces of Metta for the selected track and the search engine. The mapping tables and the information about pre-set options are obtained in advance. With the exception of PubMed, mapped queries for each search engine are submitted through a connection program that is constructed based on several connection parameters extracted from the search form and website of each search engine. The basic parameters include the HTTP request method (GET or POST), search engine server name and location, and query names and pre-set options of all search fields . Because PubMed’s public search interface was complex and subject to frequent changes without prior notice, we employed PubMed’s stable EUtil function for submitting and retrieving results.
If Metta fails to establish connection with a search engine, or it is unable to get the search results from a search engine, this is displayed as an error on the result page. Such errors may be caused either by network connection issues or by changes within the search engine interface. Once connected, the back-end pulls and presents the results from the search engines for display by the web interface (front-end). A special issue here is that Metta must extract search result records from the response pages returned from each search engine. Response pages by search engines are dynamically generated usually by encoding data records retrieved from an underlying database into an HTML template. On the one hand, different search engines employ different templates to encode their search result records, which means that a different result record extraction program is needed for each search engine. On the other hand, each search engine usually employs a small number of templates (usually one), which makes it possible to identify the template (s) for each search engine based on sample response pages from the search engine. The key to an extraction program is a set of extraction rules (e.g., regular expressions in HTML tags) that identifies the beginning and the ending of each record from the response pages. In general, search result records extraction rules can either be generated manually, semi-automatically or automatically [1, 12, 13]. In Metta, the extraction rules for the search engines are manually created to ensure their accuracy, which is critical for the applications Metta is designed to support. It is also necessary for Metta to retrieve multiple pages of results when present, knowing how many pages there are and when the result is finished. These tasks can also be solved using rules extracted from the response pages from each search engine.
This includes export of full bibliographic records in XML format intended for use by automated informatics processing tools residing later in the pipeline project. We modified the XML format used by PubMed so that it was applicable across all of the databases. In addition, we created a separate link containing a text file of abbreviated bibliographic information in BibTex format, which can be readily imported into commercial reference manager software. A significant issue in this module is to identify articles that are retrieved from multiple databases. This allows duplicate results to be removed (only one copy is kept), which saves time for systematic reviewers . Identification of multiple records that correspond to the same real-world entity is known as entity identification and record-linkage, and the problem has received much attention in the database community (e.g., [15, 16]). For Metta, the problem is solved in three steps. First, the semantic data units within each record are identified. For example, a citation record may have semantic data units Author, Title, Journal Name, etc. Second, a set of distance/similarity functions is used to compute how well two values of the same semantic from different records are matched. For example, an edit-distance function can be used to compute how similar two titles are. Third, for each pair of records, based on how similar their corresponding data units are, a decision is made on whether the two records are matched. Once citation records corresponding to the same article are identified, de-duplication in Metta was carried out by first retaining records that were indexed in PubMed (because of its better and more elaborate indexing scheme) and then following a priority order: EMBASE, Cochrane, CINAHL and lastly, PsycINFO. The details about the algorithms and code for de-duplication in Metta are described in a separate publication .
Metta is a working demonstration site that continues to undergo field testing and modification to serve systematic reviewers. However, a number of important challenges will be important to tackle if Metta is to become deployed as a stable production service: