ArticlebioRxiv : the preprint server for biology2023
Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from
Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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Who cites it
0 citing papers in PubMed, 1 citations in OpenAlex.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Systematic reviews, i.e., research summaries that address focused questions in a structured and reproducible manner, are a cornerstone of evidence-based medicine and research. However, certain systematic review steps such as data extraction are labour-intensive which hampers their applicability, not least with the rapidly expanding body of biomedical literature. Objective: To bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience Results: Our data mining tool Auto-STEED (Automated and STructured Extraction of Experimental Data) was able to extract key experimental parameters such as animal models and species as well as risk of bias items such as randomization or blinding from Conclusions: Our developed text mining tool Auto-STEED is able to extract key experimental parameters and risk of bias items from the neuroscience
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.