Trial reportAMIA ... Annual Symposium proceedings. AMIA Symposium2022
Methodological information extraction from randomized controlled trial publications: a pilot study.
Trial report in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis 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
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- Enhancing automated indexing of publication types and study designs in biomedical literature using full-text features.medRxiv : the preprint server for health sciences · 2025Article
- Predicting the sample size of randomized controlled trials using natural language processing.JAMIA open · 2024Article
- Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines.Scientific reports · 2024Article
- CONSORT-TM: Text classification models for assessing the completeness of randomized controlled trial publications.medRxiv : the preprint server for health sciences · 2024Article
- Automatic categorization of self-acknowledged limitations in randomized controlled trial publications.Journal of biomedical informatics · 2024Article
- MED-ChatGPT CoPilot: a ChatGPT medical assistant for case mining and adjunctive therapy.Frontiers in medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most biomedical information extraction (IE) approaches focus on entity types such as diseases, drugs, and genes, and relations such as gene-disease associations. In this paper, we introduce the task of methodological IE to support fine-grained quality assessment of randomized controlled trial (RCT) publications. We draw from the Ontology of Clinical Research (OCRe) and the CONSORT reporting guidelines for RCTs to create a categorization of relevant methodological characteristics. In a pilot annotation study, we annotate a corpus of 70 full-text publications with these characteristics. We also train baseline named entity recognition (NER) models to recognize these items in RCT publications using several training sets with different negative sampling strategies. We evaluate the models at span and document levels. Our results show that it is feasible to use natural language processing (NLP) and machine learning for fine-grained extraction of methodological information. We propose that our models, after improvements, can support assessment of methodological quality in RCT publications. Our annotated corpus, models, and code are publicly available at https://github.com/kellyhoang0610/RCTMethodologyIE.
Indexed as
Identifiers
37128457PMC10148349W4367668267What 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.