Figure 1.
A schematic representation of the database for Aggregate Analysis of ClinicalTrials.Gov (AACT) with its key enhancements.
Figure 2.
High-level Entity-Relationship Diagram (ERD) for AACT.
Table 1.
Escape characters and replacements.
Figure 3.
Percentage of interventional studies with complete data by registration year for selected data elements.
Figure 4.
An overview of methodology and process of developing clinical specialty datasets.
The INTERVENTIONS, CONDITIONS, and KEYWORDS tables consist of disease condition terms provided by data submitters that include both MeSH and non-MeSH terms. The INTERVENTION_BROWSE and CONDITION_BROWSE tables are populated by MeSH terms generated by NLM algorithm (a) Process illustrating how MeSH terms are created in ClinicalTrials.gov. Tables and data shown here does not represent entire ClinicalTrials.gov database (b) Process illustrating the annotation and validation of disease conditions (c) Process illustrating the creation of specialty datasets.
Table 2.
MeSH Subject Headings, 2010—Diseases.
Table 3.
Frequency of intermediate terms and top node terms that did not match annotations of lower-level terms.
Figure 5.
Source: 2010 online MeSH thesaurus (available: http://www.nlm.nih.gov/cgi/mesh/2010/MB_cgi).
Figure 6.
Rules for deciding whether a given study belongs to a given specialty.
Table 4.
Number of studies reviewed by each set of clinician reviewers.
Table 5.
Contingency table for identifying misclassification errors.
Table 6.
Classification of studies: algorithmically vs. manually.
Table 7.
Comparison between manual classification and algorithmic classification for cardiology, oncology, and mental health.
Table 8.
Summary of disagreements between clinical specialty reviewers in study classification.
Table 9.
Summary of results of comparison between condition_browse and condition data by specialty classification.