Lookup Data

Controlled Terminology

Many of the data fields in the MDR system are categorised, i.e. they are constrained to hold one of a predefined set of possible values.
For example, a clinical study may fall into one of several different main ‘types’, according to the broad methodology used:

• Interventional
• Observational
• Observational [Patient Registry]
• Expanded access
• Funded programme

Although clinical studies are usually categorised in similar ways, different systems can use different terms for those categories. Using the MDR’s controlled terminology, studies labelled as ‘clinical trial’, ‘randomised trial’, or ‘active intervention’ would all be mapped to a study type of ‘Interventional’, while other studies, described variously as ‘off-label’, ‘compassionate use’, or ‘pre-licence’, would all be mapped to ‘Expanded Access’. The options available for any categorised data item are stored in a 'look up' table.
The controlled terminologies are used in three main contexts.
   a) When data harvesting from external sources, they are used to make the various source data points as consistent as possible, by mapping the categories found to a single set of terms, as described above.
   b) When searching or filtering records in the MDR portal, they can be used to select studies or data objects of a certain type, e.g. only studies with a status of 'completed', or studies that have associated protocols.
   c) Within direct data entry of the metadata, e.g. by a researcher using a web portal to enter metadata, they can be used as the options available for many of the attributes of studies and objects.
In the data entry context only a subset of the available categories may be required. For instance it is more likely that the 'not known' option will not be necessary if the data is being added by a researcher familiar with a study or digital olbject, whereas it is required for data that has been harvested from an external source such as a trial registry. Conversely some data that exists in systems, such as internal dates linked to PubMed processing, will not be known to the researcher when he or she adds the metadata manually. Many of the lookup tables therefore contain a field ('use_in_data_entry') that indicates whether or not they should be used, for example in a drop down box, as one of the options available within data entry.

Structure of Look Up tables

The MDR system stores the available categories in a collection of ‘look up’ tables, all belonging to the 'lup' schema of the context database. Each look up table lists the options available for that data field, and almost all have a core common structure:

• An integer id field, that provides a key for each option. The database only has to store the id to represent the category being used, making storage much more efficient.
• A string name field, with the short name of the category, as it would appear – for instance – in a dropdown box that displayed the alternatives available.
• A text description field, that provides, when necessary, a brief explanation of what the category meant.
• A boolean use_in_data_entry field, that indicates if the field should be used in a data entry context, as opposed to a data harvesting context or filtering context.
• An integer list_order field, which allows the default order in a display or report (normally that of the id, as the primary key) to be over-ridden, by ordering the records according to the value of this field.

A few look up tables have one or two additional fields but the great majority follow the pattern above.
The categories used are normally as already exist within DataCite (for object attributes) or ClinicalTrials.gov (for study attributes), augmented if necessary by values from a few other key systems, or by ECRIN, to better cover the full range of categories required for clinical research data objects. For instance the listed study types are based on the study type classification found within the ClinicalTrials.gov trials registry. The lookup tables contain two additional ‘audit’ field:

• A string source field, denoting the system from which the term was originally taken. Terms created by the MDR team are labelled as ‘ECRIN’
• A date date_added field, that indicates the date a category was agreed as being required by the MDR system (though full implementation may be later). If a term has been proposed but not yet agreed this date is null.

The study_types table, below, illustrates how this structure works in the case of study categories.

id	name	description	use_in_data_entry	list_order	source	date_added
0	Not yet known	Dummy value supplied by default on entity creation.	false	99	ECRIN	2019-02-08
11	Interventional	A clinical trial.	true	10	ClinicalTrials.gov	2019-02-08
12	Observational	Any form of non-interventional research.	true	20	ClinicalTrials.gov	2019-02-08
13	Observational Patient Registry	Collecting data for a designated registry.	true	30	ClinicalTrials.gov	2019-02-08
14	Expanded access	Off label usage of a new product for individuals.	true	40	ClinicalTrials.gov	2019-02-08
15	Funded programme	With a single or linked series of grants.	false	50	ClinicalTrials.gov	2019-02-08

Listing of Look Up tables

At present the MDR system contains 29 look up tables, with most indicating that they refer to categorised fields by their name, usually <something>_types, occasionally _codes, categories or _classes.
The values in each of them (id, name, description, use in data entry and source) can be found by following the links listed below.

• study attributes (study_types, study_statuses, gender_eligibility_types)
• study features (feature_types and categories of phase, allocation method, masking, time perspective etc.)
• study_relationship_types
• object_classes
• object_types
• object_filter_types
• object_access_types
• object_instance_types
• object_relationship_types
• contribution_types
• dataset_consent_types
• dataset_de-identification_levels
• dataset_record_key_types
• date_types
• description_types
• doi_status_types
• identifier_types
• language_codes
• language_usage_types
• resource_types
• title_types
• topic_types
• topic_vocabularies
• units (for size and time)
• composite_hash_types