Ten simple rules for using large language models in science, version 1.0.

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

Who cites it

5 citing papers in PubMed.

1. A practical risk framework for large language model use in life science research.PLoS computational biology · 2026
   Article
2. Thinking Outside the Black Box: Seven Guidelines for GenAI Use in the Ecological Sciences.Ecology and evolution · 2026
   Article
3. Elements and roadmap for interactive molecular graphics and modeling "in the Holodeck".Protein science : a publication of the Protein Society · 2026
   Article
4. Roadmap for using large language models (LLMs) to accelerate cross-disciplinary research with an example from computational biology.Discover artificial intelligence · 2026
   Review
5. Ten simple rules for early-career researchers supervising short-term student projects.PLoS computational biology · 2025
   Article

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

Authors and funding

20 authors.

Funding

Abstract

PubMed holds no abstract for this paper.

Indexed as