ArticleBMC veterinary research2024
A non-invasive method to determine core temperature for cats and dogs using surface temperatures based on machine learning.
Article in BMC veterinary research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- The Expanding Role of Artificial Intelligence in Companion Animal Care: A Systematic Review.Animals : an open access journal from MDPI · 2026Review
- Associations Among Various Physical Parameters and the Axillary-to-Rectal Temperature Difference in Dogs and Cats.Journal of veterinary emergency and critical care (San Antonio, Tex. : 2001)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
9 authors.
Funding
Abstract
backgroundRectal temperature (RT) is an important index of core temperature, which has guiding significance for the diagnosis and treatment of pet diseases.
objectivesDevelopment and evaluation of an alternative method based on machine learning to determine the core temperatures of cats and dogs using surface temperatures. ANIMALS: 200 cats and 200 dogs treated between March 2022 and May 2022.
methodsA group of cats and dogs were included in this study. The core temperatures and surface body temperatures were measured. Multiple machine learning methods were trained using a cross-validation approach and evaluated in one retrospective testing set and one prospective testing set.
resultsThe machine learning models could achieve promising performance in predicting the core temperatures of cats and dogs using surface temperatures. The root mean square errors (RMSE) were 0.25 and 0.15 for cats and dogs in the retrospective testing set, and 0.15 and 0.14 in the prospective testing set.
conclusionThe machine learning model could accurately predict core temperatures for companion animals of cats and dogs using easily obtained body surface temperatures.
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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.