ArticleJournal of clinical epidemiology2019
Automatic extraction of quantitative data from ClinicalTrials.gov to conduct meta-analyses.
Article in Journal of clinical epidemiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 2 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Transforming evidence synthesis: A systematic review of the evolution of automated meta-analysis in the age of AI.Research synthesis methods · 2026Pooled it
- Automatic data extraction to support meta-analysis statistical analysis: a case study on breast cancer.BMC medical informatics and decision making · 2022Pooled it
- Misreporting of Patient-Relevant and Study Design Elements in Anesthesiology Randomized Controlled Trials: An Observational Study.Medical sciences (Basel, Switzerland) · 2025Observational
- A multi-source heterogeneous medical data enhancement framework based on lakehouse.Health information science and systems · 2024Article
- An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review.BMC medical research methodology · 2024Article
- Automation of systematic reviews of biomedical literature: a scoping review of studies indexed in PubMed.Systematic reviews · 2024Article
- A comparison of machine learning methods to find clinical trials for inclusion in new systematic reviews from their PROSPERO registrations prior to searching and screening.Research synthesis methods · 2024Article
- Machine Learning Methods for Systematic Reviews:: A Rapid Scoping Review.Delaware journal of public health · 2023Article
- Advancing herbal medicine: enhancing product quality and safety through robust quality control practices.Frontiers in pharmacology · 2023Review
- Identifying unreported links between ClinicalTrials.gov trial registrations and their published results.Research synthesis methods · 2022Article
- The scientific publication score - a new tool for summarizing evidence and data quality criteria of biomedical publications.Journal of bone and joint infection · 2022Article
- Successful incorporation of single reviewer assessments during systematic review screening: development and validation of sensitivity and work-saved of an algorithm that considers exclusion criteria and count.Systematic reviews · 2021Article
- Salvage therapy for progressive, treatment-refractory or recurrent pediatric medulloblastoma: a systematic review protocol.Systematic reviews · 2020Article
- Deep Learning Approach to Parse Eligibility Criteria in Dietary Supplements Clinical Trials Following OMOP Common Data Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Unique insights from ClinicalTrials.gov by mining protein mutations and RSids in addition to applying the Human Phenotype Ontology.PloS one · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectivesSystematic reviews and meta-analyses are labor-intensive and time-consuming. Automated extraction of quantitative data from primary studies can accelerate this process. ClinicalTrials.gov, launched in 2000, is the world's largest trial repository of results data from clinical trials; it has been used as a source instead of journal articles. We have developed a Web application called EXACT (EXtracting Accurate efficacy and safety information from ClinicalTrials.gov) that allows users without advanced programming skills to automatically extract data from ClinicalTrials.gov in analysis-ready format. We have also used the automatically extracted data to examine the reproducibility of meta-analyses in three published systematic reviews. STUDY DESIGN AND
settingWe developed a Python-based software application (EXACT) that automatically extracts data required for meta-analysis from the ClinicalTrials.gov database in a spreadsheet format. We confirmed the accuracy of the extracted data and then used those data to repeat meta-analyses in three published systematic reviews. To ensure that we used the same statistical methods and outcomes as the published systematic reviews, we repeated the meta-analyses using data manually extracted from the relevant journal articles. For the outcomes whose results we were able to reproduce using those journal article data, we examined the usability of ClinicalTrials.gov data.
resultsEXACT extracted data at ClincalTrials.gov with 100% accuracy, and it required 60% less time than the usual practice of manually extracting data from journal articles. We found that 87% of the data elements extracted using EXACT matched those extracted manually from the journal articles. We were able to reproduce 24 of 28 outcomes using the journal article data. Of these 24 outcomes, we were able to reproduce 83.3% of the published estimates using data at ClinicalTrials.gov.
conclusionEXACT (http://bio-nlp.org/EXACT) automatically and accurately extracted data elements from ClinicalTrials.gov and thus reduced time in data extraction. The ClinicalTrials.gov data reproduced most meta-analysis results in our study, but this conclusion needs further validation.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.