ArticleScientific reports2023
An automated rat grimace scale for the assessment of pain.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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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
9 citing papers in PubMed.
- Artificial intelligence-based automated scoring of the mouse grimace scale: a systematic review.Pain reports · 2026Review
- Tail-rattling in rodents: more than a threat display.Journal of comparative physiology. A, Neuroethology, sensory, neural, and behavioral physiology · 2026Review
- Behavioral assessment of pain in rodents: advances from evoked responses to spontaneous states and multimodal approaches.Frontiers in pain research (Lausanne, Switzerland) · 2026Review
- How facial expressions reveal acute pain in domestic animals with facial pain scales as a diagnostic tool.Frontiers in veterinary science · 2025Review
- PainSeeker: a head pose-invariant deep learning method for assessing rat's pain by facial expressions.Frontiers in veterinary science · 2025Article
- The grimace scale: a useful tool for assessing pain in laboratory animals.Experimental animals · 2024Review
- A cutting-edge new framework for the pain management in children: nanotechnology.Frontiers in molecular neuroscience · 2024Review
- Article
- Quantitative orbital tightening for pain assessment using machine learning with DeepLabCut.Neurobiology of pain (Cambridge, Mass.)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
5 authors.
Funding
Abstract
Pain is a complex neuro-psychosocial experience that is internal and private, making it difficult to assess in both humans and animals. In pain research, animal models are prominently used, with rats among the most commonly studied. The rat grimace scale (RGS) measures four facial action units to quantify the pain behaviors of rats. However, manual recording of RGS scores is a time-consuming process that requires training. While computer vision models have been developed and utilized for various grimace scales, there are currently no models for RGS. To address this gap, this study worked to develop an automated RGS system which can detect facial action units in rat images and predict RGS scores. The automated system achieved an action unit detection precision and recall of 97%. Furthermore, the action unit RGS classifiers achieved a weighted accuracy of 81-93%. The system's performance was evaluated using a blast traumatic brain injury study, where it was compared to trained human graders. The results showed an intraclass correlation coefficient of 0.82 for the total RGS score, indicating that the system was comparable to human graders. The automated tool could enhance pain research by providing a standardized and efficient method for the assessment of RGS.
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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.