ArticleScientific data2025
Annotated corpus for traditional formula-disease relationships in biomedical articles.
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.
- From narrative evidence to computable knowledge: a decision-relevant corpus for medicinal herb-disease relationships.Journal of biomedical semantics · 2026Article
- Comparisons of pharmacokinetics of glimepiride in combination with Ojeok-san versus glimepiride alone: an open-label, one-sequence, two-treatment controlled clinical study.Scientific reports · 2025Article
- Annotated corpus for traditional formula-disease relationships in biomedical articles.Scientific data · 2025Article
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
The Traditional Formula (TF), a combination of herbs prepared in accordance with traditional medicine principles, is increasingly garnering global attention as an alternative to modern medicine. Specifically, there is growing interest in exploring TF's therapeutic effects across various diseases. A significant portion of the state-of-the-art knowledge regarding the relationship between TF and disease is found in scientific publications, where manual knowledge extraction is impractical. Thus, Natural Language Processing (NLP) is being employed to efficiently and accurately search and extract crucial knowledge from unstructured literatures. However, the absence of a high-quality manually annotated corpus focusing on TF-disease relationships hampers the use of NLP in the fields of traditional medicine and modern biomedical science. This article introduces the Traditional Formula-Disease Relationship (TFDR) corpus, a manually annotated corpus designed to facilitate the automatic extraction of TF-disease relationships from biomedical literatures. The TFDR corpus includes information gleaned from 740 PubMed abstracts, encompassing a total of 6,211 TF mentions, 7,166 disease mentions, and 1,109 relationships between them encapsulated within 744 key-sentences.
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.