ArticleBMC bioinformatics2011
Automatic classification of sentences to support Evidence Based Medicine.
Article in BMC bioinformatics, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 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
53 citing papers in PubMed, 2 syntheses or guidelines pooled 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
- 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
- Evaluation of SURUS: a named entity recognition NLP system to extract knowledge from interventional study records.BMC medical research methodology · 2025Article
- Collaborative large language models for automated data extraction in living systematic reviews.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- 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
- Efficient evidence selection for systematic reviews in traditional Chinese medicine.BMC medical research methodology · 2025Article
- Multi-label Sequential Sentence Classification via Large Language Model.Findings of ACL. EMNLP. Conference on Empirical Methods in Natural Language Processing · 2024Article
- Collaborative Large Language Models for Automated Data Extraction in Living Systematic Reviews.medRxiv : the preprint server for health sciences · 2024Article
- Text classification models for assessing the completeness of randomized controlled trial publications based on CONSORT reporting guidelines.Scientific reports · 2024Article
- Natural language processing (NLP) to facilitate abstract review in medical research: the application of BioBERT to exploring the 20-year use of NLP in medical research.Systematic reviews · 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
- Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach.Bioinformatics (Oxford, England) · 2023Article
- Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs.Proceedings of machine learning research · 2023Article
- A Scoping Review of Adopted Information Extraction Methods for RCTs.Medical journal of the Islamic Republic of Iran · 2023Article
- A reproducible experimental survey on biomedical sentence similarity: A string-based method sets the state of the art.PloS one · 2022Article
- Toward assessing clinical trial publications for reporting transparency.Journal of biomedical informatics · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimGiven a set of pre-defined medical categories used in Evidence Based Medicine, we aim to automatically annotate sentences in medical abstracts with these labels.
methodWe constructed a corpus of 1,000 medical abstracts annotated by hand with specified medical categories (e.g. Intervention, Outcome). We explored the use of various features based on lexical, semantic, structural, and sequential information in the data, using Conditional Random Fields (CRF) for classification.
resultsFor the classification tasks over all labels, our systems achieved micro-averaged f-scores of 80.9% and 66.9% over datasets of structured and unstructured abstracts respectively, using sequential features. In labeling only the key sentences, our systems produced f-scores of 89.3% and 74.0% over structured and unstructured abstracts respectively, using the same sequential features. The results over an external dataset were lower (f-scores of 63.1% for all labels, and 83.8% for key sentences).
conclusionsOf the features we used, the best for classifying any given sentence in an abstract were based on unigrams, section headings, and sequential information from preceding sentences. These features resulted in improved performance over a simple bag-of-words approach, and outperformed feature sets used in previous work.
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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.