Abstract
BACKGROUND AND AIM: Generally, rectal body temperature (BTrectum) is used to prefer as core body temperature in dogs. However, this procedure is time- and labor-consuming with stress induction. Therefore, infrared auricular temperature (BTear) and surface temperature (ST) could be applied to estimate BTrectum. This study aimed to estimate BTrectum from BTear or ST in various areas and determined the factors that influenced the accuracy of prediction equations. MATERIALS AND METHODS: Under controlled temperature (n=197) and ambient temperature (n=183), the parameters BTrectum, BTear, and ST at internal pinna, auricular canal, lateral aspect of shoulder, hip, axillary area, inguinal area, footpad, and anal area (STrectum) were measured. In addition, temperature and humidity levels of the surrounding environment were recorded. The correlation between each measurement technique was calculated. The BTrectum prediction equation was created using all measured data and several influencing factors (environmental condition, size, coat type, and body condition score [BCS]). RESULTS: The highest correlation with BTrectum was observed for BTear (r=0.61, p<0.01), which was similar to STrectum (r=0.61, p<0.01). Based on multiple linear regression model results using BTrectum as the dependent variable, BTear or STrectum were first selected as independent variables in all estimation equations. Ambient temperatures (R(2)=0.397), small breed (R(2)=0.582), long hair (R(2)=0.418), and/or a BCS of 2 (R(2)=0.557) provided the highest coefficients of determination of the prediction equation. CONCLUSION: The most appropriate predictors for estimating BTrectum were STrectum and BTear, which were impacted by the dog's signalments and the environment. To obtain satisfactory outcomes, the equation must be selected depending on the dog's signalments and the environmental conditions. However, based on the findings of this investigation, the accuracy remains low in several equations, and further studies are needed to improve the accuracy of the equation, mainly by increasing the sample size and developing a specific equation for each dog's signaling and environmental condition.