{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T20:10:59Z","timestamp":1771013459574,"version":"3.50.1"},"reference-count":33,"publisher":"Public Library of Science (PLoS)","issue":"2","license":[{"start":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T00:00:00Z","timestamp":1770940800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:sec id=\"sec001\">\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Network meta-analysis (NMA) can compare several interventions at once by combining head-to-head and indirect trial evidence. However, identifying, extracting, and modelling these often takes months, delaying updates in many therapeutic areas.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec002\">\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>\n                      To develop and validate\n                      <jats:italic>MetaMind<\/jats:italic>\n                      , an end-to-end, transformer-driven framework that automates NMA processes\u2014including study retrieval, structured data extraction, and meta-analysis execution\u2014while minimizing human input.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec003\">\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      <jats:italic>MetaMind<\/jats:italic>\n                      integrates Promptriever, a fine-tuned retrieval model, to semantically retrieve high-impact clinical trials from PubMed; a multi-agent LLM architecture--Mixture of Agents (MoA)-- pipeline to extract PICO-structured (Population, Intervention, Comparison, Outcome) endpoints; and GPT-4o\u2013generated Python and R scripts to perform Bayesian random-effects NMA and other NMA designs within a unified workflow. Validation was conducted by comparing\n                      <jats:italic>MetaMind\u2019s<\/jats:italic>\n                      outputs against manually performed NMAs in ulcerative colitis (UC) and Crohn\u2019s disease (CD).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec004\">\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Promptriever outperformed baseline SentenceTransformer with higher similarity scores (0.7403 vs. 0.7049 for UC; 0.7142 vs. 0.7049 for CD) and narrower relevance ranges. Promptriever performance achieved 82.1% recall, 91.1% precision and an F1 score of 86.4% when compared to a previously published NMA.\n                      <jats:italic>MetaMind<\/jats:italic>\n                      achieved 100% accuracy on a limited set of remission endpoints regarding PICO (Population, Intervention, Comparator, Outcome) element extraction and produced comparative effect estimates and credible intervals closely matching manual analyses.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec id=\"sec005\">\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>\n                      In our validation studies,\n                      <jats:italic>MetaMind<\/jats:italic>\n                      reduced the end-to-end NMA process to less than a week, compared with the several months typically needed for manual workflows, while preserving statistical rigor. This suggests its potential for future scaling of evidence synthesis to additional therapeutic areas.\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.1371\/journal.pone.0342895","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T18:33:53Z","timestamp":1771007633000},"page":"e0342895","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["MetaMind: A multi-agent transformer-driven framework for automated network meta-analyses"],"prefix":"10.1371","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1444-1408","authenticated-orcid":true,"given":"Achilleas","family":"Livieratos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Maria","family":"Kudela","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuxi","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"All-shine","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Luo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junjing","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Di","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sai","family":"Dharmarajan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sotirios","family":"Tsiodras","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1789-3004","authenticated-orcid":true,"given":"Vivek","family":"Rudrapatna","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Margaret","family":"Gamalo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"340","published-online":{"date-parts":[[2026,2,13]]},"reference":[{"issue":"2","key":"pone.0342895.ref001","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s41669-024-00476-9","article-title":"Artificial Intelligence to Automate Network Meta-Analyses: Four Case Studies to Evaluate the Potential Application of Large Language Models","volume":"8","author":"T Reason","year":"2024","journal-title":"Pharmacoecon Open"},{"issue":"1","key":"pone.0342895.ref002","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1038\/s41746-024-01239-w","article-title":"Closing the gap between open source and commercial large language models for medical evidence summarization","volume":"7","author":"G Zhang","year":"2024","journal-title":"NPJ Digit Med"},{"issue":"1","key":"pone.0342895.ref003","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1186\/s12874-017-0431-4","article-title":"Frequency of data extraction errors and methods to increase data extraction quality: a methodological review","volume":"17","author":"T Mathes","year":"2017","journal-title":"BMC Med Res Methodol"},{"issue":"5","key":"pone.0342895.ref004","first-page":"365","article-title":"Automated Mass Extraction of Over 680,000 PICOs from Clinical Study Abstracts Using Generative AI: A Proof-of-Concept Study","volume":"38","author":"T Reason","year":"2024","journal-title":"Pharmaceut Med"},{"key":"pone.0342895.ref005","unstructured":"Wang J, Wang J, Athiwaratkun B, Zhang C, Zou J. 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