ArticleBMC medical informatics and decision making2010
ExaCT: automatic extraction of clinical trial characteristics from journal publications.
Article in BMC medical informatics and decision making, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 74 papers, 5 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
74 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Large Language Model Analysis of Reporting Quality of Randomized Clinical Trial Articles: A Systematic Review.JAMA network open · 2025Pooled it
- Evaluation of a prototype machine learning tool to semi-automate data extraction for systematic literature reviews.Systematic reviews · 2023Pooled it
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- Automating data extraction in systematic reviews: a systematic review.Systematic reviews · 2015Pooled it
- Using text mining for study identification in systematic reviews: a systematic review of current approaches.Systematic reviews · 2015Pooled it
- Methodological information extraction from randomized controlled trial publications: a pilot study.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Trial
- Advancing Evidence-Based Medicine for Population, Intervention, Comparison, and Outcome Element Recognition and Extraction in Medical Literature: Large Language Model Approach.Journal of medical Internet research · 2026Article
- The landscape of artificial intelligence tools and platforms for evidence synthesis: a scoping review.Systematic reviews · 2026Article
- Applying machine learning to enhance core outcome set development: automating the data extraction and classification of outcomes.Journal of orthopaedic surgery and research · 2025Article
- A foundation model for human-AI collaboration in medical literature mining.Nature communications · 2025Article
- Use of deep learning-based NLP models for full-text data elements extraction for systematic literature review tasks.Scientific reports · 2025Article
- TrialSieve: A Comprehensive Biomedical Information Extraction Framework for PICO, Meta-Analysis, and Drug Repurposing.Bioengineering (Basel, Switzerland) · 2025Article
- Digital Tools to Support the Systematic Review Process: An Introduction.Journal of evaluation in clinical practice · 2025Review
- SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.Scientific data · 2025Article
- SPIRIT-CONSORT-TM: a corpus for assessing transparency of clinical trial protocol and results publications.medRxiv : the preprint server for health sciences · 2025Article
- Automating updates for scoping reviews on the environmental drivers of human and animal diseases: a comparative analysis of AI methods.Frontiers in artificial intelligence · 2025Article
- Predicting the sample size of randomized controlled trials using natural language processing.JAMIA open · 2024Article
- Article
- Article
- An exploration of available methods and tools to improve the efficiency of systematic review production: a scoping review.BMC medical research methodology · 2024Article
14 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
backgroundClinical trials are one of the most important sources of evidence for guiding evidence-based practice and the design of new trials. However, most of this information is available only in free text - e.g., in journal publications - which is labour intensive to process for systematic reviews, meta-analyses, and other evidence synthesis studies. This paper presents an automatic information extraction system, called ExaCT, that assists users with locating and extracting key trial characteristics (e.g., eligibility criteria, sample size, drug dosage, primary outcomes) from full-text journal articles reporting on randomized controlled trials (RCTs).
methodsExaCT consists of two parts: an information extraction (IE) engine that searches the article for text fragments that best describe the trial characteristics, and a web browser-based user interface that allows human reviewers to assess and modify the suggested selections. The IE engine uses a statistical text classifier to locate those sentences that have the highest probability of describing a trial characteristic. Then, the IE engine's second stage applies simple rules to these sentences to extract text fragments containing the target answer. The same approach is used for all 21 trial characteristics selected for this study.
resultsWe evaluated ExaCT using 50 previously unseen articles describing RCTs. The text classifier (first stage) was able to recover 88% of relevant sentences among its top five candidates (top5 recall) with the topmost candidate being relevant in 80% of cases (top1 precision). Precision and recall of the extraction rules (second stage) were 93% and 91%, respectively. Together, the two stages of the extraction engine were able to provide (partially) correct solutions in 992 out of 1050 test tasks (94%), with a majority of these (696) representing fully correct and complete answers.
conclusionsOur experiments confirmed the applicability and efficacy of ExaCT. Furthermore, they demonstrated that combining a statistical method with 'weak' extraction rules can identify a variety of study characteristics. The system is flexible and can be extended to handle other characteristics and document types (e.g., study protocols).
Indexed as
Identifiers
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.