ArticleScientific data2025
A Dataset of Medical Questions Paired with Automatically Generated Answers and Evidence-supported References.
Article in Scientific data, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- Article
- Article
- MedDiscover: A Domain-Specific Retrieval-Augmented Generation Framework for Evidence-Grounded Knowledge Extraction in Metabolomics.Computational and structural biotechnology journal · 2026Article
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.
Funding
No grant is acknowledged in the PubMed record.
Abstract
New Large Language Models (LLM)-based approaches to medical Question Answering show unprecedented improvements in the fluency, grammaticality, and other qualities of the generated answers. However, the systems occasionally produce coherent, topically relevant, and plausible answers that are not based on facts and may be misleading and even harmful. New types of datasets are needed to evaluate the truthfulness of generated answers and develop reliable approaches for detecting answers that are not supported by evidence. The MedAESQA (Medical Attributable and Evidence Supported Question Answering) dataset presented in this work is designed for developing, fine-tuning, and evaluating language generation models for their ability to attribute or support the stated facts by linking the statements to the relevant passages of reliable sources. The dataset comprises 40 naturally occurring aggregated deidentified questions. Each question has 30 human and LLM-generated answers in which each statement is linked to a scientific abstract that supports it. The dataset provides manual judgments on the accuracy of the statements and the relevancy of the scientific papers.
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.