ArticleLaboratory animal research2024
Artificial intelligence generated clinical score sheets: looking at the two faces of Janus.
Article in Laboratory animal research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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
3 citing papers in PubMed.
- One size does not fit all: Guidelines for designing a score sheet for animal experiments - eight essential steps.Laboratory animals · 2026Article
- Between Prometheus and Thanatos: Reflections on Intracardiac Injection Models for Metastasis in Mice.Journal of the American Association for Laboratory Animal Science : JAALAS · 2025Review
- Large Language Models and Their Applications in Drug Discovery and Development: A Primer.Clinical and translational science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In vivo experiments are increasingly using clinical score sheets to ensure minimal distress to the animals. A score sheet is a document that includes a list of specific symptoms, behaviours and intervention guidelines, all balanced to for an objective clinical assessment of experimental animals. Artificial Intelligence (AI) technologies are increasingly being applied in the field of preclinical research, not only in analysis but also in documentation processes, reflecting a significant shift towards more technologically advanced research methodologies. The present study explores the application of Large Language Models (LLM) in generating score sheets for an animal welfare assessment in a preclinical research setting. Focusing on a mouse model of inflammatory bowel disease, the study evaluates the performance of three LLM - ChatGPT-4, ChatGPT-3.5, and Google Bard - in creating clinical score sheets based on specified criteria such as weight loss, stool consistency, and visible fecal blood. Key parameters evaluated include the consistency of structure, accuracy in representing severity levels, and appropriateness of intervention thresholds. The findings reveal a duality in LLM-generated score sheets: while some LLM consistently structure their outputs effectively, all models exhibit notable variations in assigning numerical values to symptoms and defining intervention thresholds accurately. This emphasizes the dual nature of AI performance in this field-its potential to create useful foundational drafts and the critical need for professional review to ensure precision and reliability. The results highlight the significance of balancing AI-generated tools with expert oversight in preclinical research.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.