Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews

[2608.14737] Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Computer Science > Computation and Language arXiv:2608.14737 (cs) [Submitted on 13 Aug 2026] Title: Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews Authors: Gilberto Sussumu Hida , Danilo Monteiro Ribeiro , Clayton Suguio Hida View a PDF of the paper titled Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews, by Gilberto Sussumu Hida and 2 other authors View PDF HTML (experimental) Abstract: This study analyses LLMs in imbalanced binary classification, using study screening in systematic reviews as the application domain. An experiment was conducted in five reviews, comparing individual and batch processing, with and without prevalence metadata. The results indicate a limited influence of the prevalence metadata, with no evidence that it improves performance. In contrast, batch processing produced larger behavioral changes that varied according to the prevalence of the class. The aggregate and item-level analyses did not always coincide. Therefore, batch processing should be evaluated not only in terms of cost, but also in relation to its effects on decision-making behavior. Comments: 12 pages, 4 figures. Accepted at ENIAC 2026 (National Meeting on Artificial and Computational Intelligence), part of BRACIS 2026 Subjects: Computation and Language (cs.CL) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2608.14737 [cs.CL] (or arXiv:2608.14737v1 [cs.CL] for this version) https://doi.org/10.48550/arXiv.2608.14737 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Gilberto Sussumu Hida [ view email ] [v1] Thu, 13 Aug 2026 09:48:22 UTC (1,290 KB) Full-text links: Access Paper: View a PDF of the paper titled Class Imbalance and Batch Effects in LLM-Based Screening for Systematic Reviews, by Gilberto Sussumu Hida and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.CL < prev | next > new | recent | 2026-08 Change to browse by: cs cs.AI References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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