ArticleProceedings of machine learning research2023
Jointly Extracting Interventions, Outcomes, and Findings from RCT Reports with LLMs.
Article in Proceedings of machine learning research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Data extraction methods for systematic review (semi)automation: Update of a living systematic review.F1000Research · 2021Pooled it
- A foundation model for human-AI collaboration in medical literature mining.Nature communications · 2025Article
- Evidence triangulator: using large language models to extract and synthesize causal evidence across study designs.Nature communications · 2025Article
- Accelerating clinical evidence synthesis with large language models.NPJ digital medicine · 2025Article
- The Impact of Temperature on Extracting Information From Clinical Trial Publications Using Large Language Models.Cureus · 2024Article
- Automatically Extracting Numerical Results from Randomized Controlled Trials with Large Language Models.Proceedings of machine learning research · 2024Article
- PubMed and beyond: biomedical literature search in the age of artificial intelligence.EBioMedicine · 2024Review
- Article
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
Results from Randomized Controlled Trials (RCTs) establish the comparative effectiveness of interventions, and are in turn critical inputs for evidence-based care. However, results from RCTs are presented in (often unstructured) natural language articles describing the design, execution, and outcomes of trials; clinicians must manually extract findings pertaining to interventions and outcomes of interest from such articles. This onerous manual process has motivated work on (semi-)automating extraction of structured evidence from trial reports. In this work we propose and evaluate a text-to-text model built on instruction-tuned Large Language Models (LLMs) to jointly extract
Identifiers
40988778PMC12451563What 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.