ArticleComputational and structural biotechnology journal2026
MedDiscover: A Domain-Specific Retrieval-Augmented Generation Framework for Evidence-Grounded Knowledge Extraction in Metabolomics.
Article in Computational and structural biotechnology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- Digital Health Revolution in Saudi Arabia: A Narrative Review of Healthcare Transformation.Healthcare (Basel, Switzerland) · 2026Review
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
Retrieval-augmented generation (RAG) can improve biomedical question answering, but claims about domain-specific retrievers require transparent, reproducible evaluation. We present MedDiscover, an open-source RAG implementation and benchmark instantiated for metabolomics and metabolic-disorder literature, with a 2-tier dataset design: a Gold set of 10 papers and 30 expert-curated question-answer (QA) pairs with traceable references, and a Silver set of 100 papers curated using International Statistical Classification of Diseases and Related Health Problems 10th Revision metabolic disorder codes E70 to E88 with ~600 synthetic QA pairs and retrievability metadata (Ada
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.