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screenllm: LLM-Assisted Title/Abstract Screening for Systematic Reviews
A turn-key workflow for LLM-assisted systematic-review screening. The package ranks a corpus of titles and abstracts with an ensemble of open-source large language models served locally by 'Ollama', then applies the SAFE stopping rule to identify the records a human should screen. Defaults match the four-LLM mean ensemble and the SAFE configuration recommended by Spillias et al. (2026). A companion 'Shiny' app walks the human reviewer through the records above the stopping point. Complementary to the 'AIscreenR' package of Vembye et al. (2025) <doi:10.1037/met0000769>, which targets cloud-hosted 'GPT' models via the 'OpenAI' API; 'screenllm' targets locally-served open-weights ensembles with an integrated stopping rule.
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Package details |
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| Author | Scott Spillias [aut, cre], Laura Avila Turriago [aut], Christopher Brown [aut], Ariane Easton [aut], Jack Roberts [aut], Michael Sievers [aut], Steve Swearer [aut], Andrew Taylor [aut], Brigette Wright [aut], Valeriya Komyakova [aut] |
| Maintainer | Scott Spillias <scott.spillias@csiro.au> |
| License | MIT + file LICENSE |
| Version | 0.1.0 |
| URL | https://github.com/s-spillias/screenllm |
| Package repository | View on CRAN |
| Installation |
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