ArticleBioinformatics advances2021
Identifying and classifying goals for scientific knowledge.
Article in Bioinformatics advances, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Thinking transdisciplinarily: Question-Collaboration Workshop to ideate across disciplines.Frontiers in psychology · 2026Article
- Utilizing ChatGPT as a scientific reasoning engine to differentiate conflicting evidence and summarize challenges in controversial clinical questions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- RecSOI: recommending research directions using statements of ignorance.Journal of biomedical semantics · 2024Article
- Creating an ignorance-base: Exploring known unknowns in the scientific literature.Journal of biomedical informatics · 2023Article
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
motivationScience progresses by posing good questions, yet work in biomedical text mining has not focused on them much. We propose a novel idea for biomedical natural language processing: identifying and characterizing the
resultsWe present a novel ignorance taxonomy driven by the role statements of ignorance play in research, identifying specific goals for future scientific knowledge. Using this taxonomy and reliable annotation guidelines (inter-annotator agreement above 80%), we created a gold standard ignorance corpus of 60 full-text documents from the prenatal nutrition literature with over 10 000 annotations and used it to train classifiers that achieved over 0.80 F1 scores. AVAILABILITY AND IMPLEMENTATION: Corpus and source code freely available for download at https://github.com/UCDenver-ccp/Ignorance-Question-Work. The source code is implemented in Python.
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.