{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T19:07:22Z","timestamp":1768504042947,"version":"3.49.0"},"posted":{"date-parts":[[2023,2,27]]},"group-title":"Neuroscience","reference-count":23,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,2,27]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Objective<\/jats:title>\n                  <jats:p>\n                    To bridge this gap, we aimed at developing a data mining tool in the R programming environment to automate data extraction from neuroscience\n                    <jats:italic>in vivo<\/jats:italic>\n                    publications. The function was trained on a literature corpus (n=45 publications) of animal motor neuron disease studies and tested in two validation corpora (motor neuron diseases, n=31 publications; multiple sclerosis, n=244 publications).\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>\n                    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\n                    <jats:italic>in vivo<\/jats:italic>\n                    studies. Sensitivity and specificity were over 85 and 80%, respectively, for most items in both validation corpora. Accuracy and F-scores were above 90% and 0.9 for most items in the validation corpora. Time savings were above 99%.\n                  <\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>\n                    Our developed text mining tool Auto-STEED is able to extract key experimental parameters and risk of bias items from the neuroscience\n                    <jats:italic>in vivo<\/jats:italic>\n                    literature. With this, the tool can be deployed to probe a field in a research improvement context or to replace one human reader during data extraction resulting in substantial time-savings and contribute towards automation of systematic reviews. The function is available on Github.\n                  <\/jats:p>\n                <\/jats:sec>","DOI":"10.1101\/2023.02.24.529867","type":"posted-content","created":{"date-parts":[[2023,2,27]],"date-time":"2023-02-27T13:15:21Z","timestamp":1677503721000},"source":"Crossref","is-referenced-by-count":1,"title":["Auto-STEED: A data mining tool for automated extraction of experimental parameters and risk of bias items from\n                  <i>in vivo<\/i>\n                  publications"],"prefix":"10.64898","author":[{"given":"Wolfgang Emanuel","family":"Zurrer","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amelia Elaine","family":"Cannon","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ewoud","family":"Ewing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Marianna","family":"Rosso","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel S.","family":"Reich","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Benjamin V.","family":"Ineichen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"54368","reference":[{"issue":"4","key":"2023030803550699000_2023.02.24.529867v1.1","doi-asserted-by":"crossref","first-page":"498","DOI":"10.1038\/s41559-020-1153-2","article-title":"A new ecosystem for evidence synthesis","volume":"4","year":"2020","journal-title":"Nature Ecology & Evolution"},{"key":"2023030803550699000_2023.02.24.529867v1.2","doi-asserted-by":"crossref","unstructured":"Egger M , Higgins JP , and Smith GD. Systematic reviews in health research: Meta-analysis in context. John Wiley & Sons; 2022.","DOI":"10.1002\/9781119099369"},{"issue":"9","key":"2023030803550699000_2023.02.24.529867v1.3","doi-asserted-by":"crossref","first-page":"1949","DOI":"10.1097\/j.pain.0000000000001974","article-title":"A practical guide to preclinical systematic review and meta-analysis","volume":"161","year":"2020","journal-title":"Pain"},{"issue":"5","key":"2023030803550699000_2023.02.24.529867v1.4","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1177\/01410768221093551","article-title":"The role of systematic reviews in identifying the limitations of preclinical animal research, 2000\u20132022: part 1","volume":"115","year":"2022","journal-title":"Journal of the Royal Society of Medicine"},{"issue":"1","key":"2023030803550699000_2023.02.24.529867v1.5","first-page":"1","article-title":"Systematic reviews for basic scientists: a different beast","volume":"103","year":"2022","journal-title":"Physiological reviews"},{"key":"2023030803550699000_2023.02.24.529867v1.6","doi-asserted-by":"publisher","DOI":"10.1136\/bmjos-2020-100103"},{"issue":"12","key":"2023030803550699000_2023.02.24.529867v1.7","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.3390\/ani9121163","article-title":"How can systematic reviews teach us more about the implementation of the 3Rs and animal welfare?","volume":"9","year":"2019","journal-title":"Animals"},{"key":"2023030803550699000_2023.02.24.529867v1.8","doi-asserted-by":"publisher","DOI":"10.1093\/ilar\/ilz015"},{"key":"2023030803550699000_2023.02.24.529867v1.9","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-012545"},{"key":"2023030803550699000_2023.02.24.529867v1.10","doi-asserted-by":"publisher","DOI":"10.1038\/nj7612-457a"},{"issue":"11","key":"2023030803550699000_2023.02.24.529867v1.11","doi-asserted-by":"crossref","first-page":"2215","DOI":"10.1002\/asi.23329","article-title":"Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references","volume":"66","year":"2015","journal-title":"Journal of the Association for Information Science and Technology"},{"key":"2023030803550699000_2023.02.24.529867v1.12","doi-asserted-by":"publisher","DOI":"10.1126\/scitranslmed.3004631"},{"key":"2023030803550699000_2023.02.24.529867v1.13","doi-asserted-by":"publisher","DOI":"10.1136\/bmjos-2020-100131"},{"key":"2023030803550699000_2023.02.24.529867v1.14","doi-asserted-by":"crossref","unstructured":"Marshall IJ , Johnson BT , Wang Z , Rajasekaran S , and Wallace BC. Semi-Automated Evidence Synthesis in Health Psychology: Current Methods and Future Prospects. Health psychology review. 2020:1\u201335.","DOI":"10.1080\/17437199.2020.1716198"},{"key":"2023030803550699000_2023.02.24.529867v1.15","doi-asserted-by":"crossref","unstructured":"Ineichen BV , Sati P , Granberg T , Absinta M , Lee NJ , Lefeuvre JA , et al. Magnetic resonance imaging in multiple sclerosis animal models: A systematic review, meta-analysis, and white paper. NeuroImage: Clinical. 2020:102371.","DOI":"10.1016\/j.nicl.2020.102371"},{"issue":"9","key":"2023030803550699000_2023.02.24.529867v1.16","doi-asserted-by":"crossref","first-page":"1769","DOI":"10.1177\/0271678X20943823","article-title":"The ARRIVE guidelines 2.0: Updated guidelines for reporting animal research","volume":"40","year":"2020","journal-title":"Journal of Cerebral Blood Flow & Metabolism"},{"key":"2023030803550699000_2023.02.24.529867v1.17","doi-asserted-by":"crossref","unstructured":"Wang Q , Liao J , Lapata M , and Macleod M. Risk of bias assessment in preclinical literature using natural language processing. 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Protocol for an analysis of in vivo reporting standards by journal, institution and funder. OSF (https:\/\/osfio\/preprints\/metaarxiv\/cjxtf\/). 2021.","DOI":"10.31222\/osf.io\/cjxtf"},{"key":"2023030803550699000_2023.02.24.529867v1.23","unstructured":"brief NSD-Tni. Space-junk spear, depression drug and the EU\u2019s digital copyright. 2019."}],"container-title":[],"original-title":[],"link":[{"URL":"https:\/\/syndication.highwire.org\/content\/doi\/10.1101\/2023.02.24.529867","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T04:19:22Z","timestamp":1768450762000},"score":1,"resource":{"primary":{"URL":"http:\/\/biorxiv.org\/lookup\/doi\/10.1101\/2023.02.24.529867"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,2,27]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1101\/2023.02.24.529867","relation":{},"subject":[],"published":{"date-parts":[[2023,2,27]]},"subtype":"preprint"}}