Cristian Martinez Alvarez, Britt A. L. Thevelein, Katie M. Hodges, Amy N. Weitzman, Amie Koenig, Benjamin M. Brainard
{"title":"狗和猫的各种生理参数与腋窝-直肠温差之间的关系。","authors":"Cristian Martinez Alvarez, Britt A. L. Thevelein, Katie M. Hodges, Amy N. Weitzman, Amie Koenig, Benjamin M. Brainard","doi":"10.1111/vec.70020","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>To compare the measurement of axillary temperature (AT) with rectal temperature (RT) in dogs and cats and to identify the influence of physical parameters on the difference between their measurement using a veterinary-specific thermometer.</p>\n </section>\n \n <section>\n \n <h3> Design</h3>\n \n <p>Prospective study (2022–2024).</p>\n </section>\n \n <section>\n \n <h3> Setting</h3>\n \n <p>University teaching hospital.</p>\n </section>\n \n <section>\n \n <h3> Animals</h3>\n \n <p>A total of 106 dogs and 101 cats aged ≥4 months.</p>\n </section>\n \n <section>\n \n <h3> Interventions</h3>\n \n <p>Body temperatures were measured contemporaneously using a veterinary-specific, calibrated, dual-thermistor axillary thermometer (AT) and a calibrated medical thermometer (RT). Data were evaluated for bias as a whole set and after stratification based on various physical parameters (species, body weight, haircoat length, body condition score [BCS]). A machine learning (ML) model was subsequently applied, and bias was reevaluated.</p>\n </section>\n \n <section>\n \n <h3> Measurements and Main Results</h3>\n \n <p>A Bland–Altman analysis comparing the measurement of AT with RT showed a bias of 1.01°C (1.82°F) and 95% limits of agreement (LOA) −0.66°C to 2.68°C (−1.19°F to 4.83°F) in dogs and a bias of 0.23°C (0.42°F) and 95% LOA of −2.61°C to 3.08°C (−4.71°F to 5.55°F) in cats. Animals weighing <10 kg had a bias of 0.33°C (95% LOA: −2.29°C to 2.96°C), animals with BCS <5 had a bias of 0.09°C (95% LOA: −2.92°C to 3.11°C), and those with shorter haircoats had a bias of 0.49°C (95% LOA: −2.03°C to 3.00°C). The ML model overestimated RT when using AT and physical parameters in its algorithm (dogs: −0.57°C [95% LOA: −2.18°C to 0.99°C]; cats −0.84°C [95% LOA: −2.62°C to 0.93°C]).</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Animals weighing <10 kg, with lower BCS, and shorter haircoats had less bias when AT was compared with RT. Cats exhibited a lower bias between AT and RT than dogs. ML models can be programmed to account for various physical characteristics, improving the predictive impact of AT for directly measured RT, especially in categories of animals where closer prediction does not already exist.</p>\n </section>\n </div>","PeriodicalId":17603,"journal":{"name":"Journal of veterinary emergency and critical care","volume":"35 4","pages":"361-367"},"PeriodicalIF":1.2000,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/vec.70020","citationCount":"0","resultStr":"{\"title\":\"Associations Among Various Physical Parameters and the Axillary-to-Rectal Temperature Difference in Dogs and Cats\",\"authors\":\"Cristian Martinez Alvarez, Britt A. L. Thevelein, Katie M. Hodges, Amy N. Weitzman, Amie Koenig, Benjamin M. Brainard\",\"doi\":\"10.1111/vec.70020\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Objective</h3>\\n \\n <p>To compare the measurement of axillary temperature (AT) with rectal temperature (RT) in dogs and cats and to identify the influence of physical parameters on the difference between their measurement using a veterinary-specific thermometer.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Design</h3>\\n \\n <p>Prospective study (2022–2024).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Setting</h3>\\n \\n <p>University teaching hospital.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Animals</h3>\\n \\n <p>A total of 106 dogs and 101 cats aged ≥4 months.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Interventions</h3>\\n \\n <p>Body temperatures were measured contemporaneously using a veterinary-specific, calibrated, dual-thermistor axillary thermometer (AT) and a calibrated medical thermometer (RT). Data were evaluated for bias as a whole set and after stratification based on various physical parameters (species, body weight, haircoat length, body condition score [BCS]). A machine learning (ML) model was subsequently applied, and bias was reevaluated.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Measurements and Main Results</h3>\\n \\n <p>A Bland–Altman analysis comparing the measurement of AT with RT showed a bias of 1.01°C (1.82°F) and 95% limits of agreement (LOA) −0.66°C to 2.68°C (−1.19°F to 4.83°F) in dogs and a bias of 0.23°C (0.42°F) and 95% LOA of −2.61°C to 3.08°C (−4.71°F to 5.55°F) in cats. Animals weighing <10 kg had a bias of 0.33°C (95% LOA: −2.29°C to 2.96°C), animals with BCS <5 had a bias of 0.09°C (95% LOA: −2.92°C to 3.11°C), and those with shorter haircoats had a bias of 0.49°C (95% LOA: −2.03°C to 3.00°C). The ML model overestimated RT when using AT and physical parameters in its algorithm (dogs: −0.57°C [95% LOA: −2.18°C to 0.99°C]; cats −0.84°C [95% LOA: −2.62°C to 0.93°C]).</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Animals weighing <10 kg, with lower BCS, and shorter haircoats had less bias when AT was compared with RT. Cats exhibited a lower bias between AT and RT than dogs. ML models can be programmed to account for various physical characteristics, improving the predictive impact of AT for directly measured RT, especially in categories of animals where closer prediction does not already exist.</p>\\n </section>\\n </div>\",\"PeriodicalId\":17603,\"journal\":{\"name\":\"Journal of veterinary emergency and critical care\",\"volume\":\"35 4\",\"pages\":\"361-367\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2025-08-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1111/vec.70020\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of veterinary emergency and critical care\",\"FirstCategoryId\":\"97\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1111/vec.70020\",\"RegionNum\":3,\"RegionCategory\":\"农林科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"VETERINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of veterinary emergency and critical care","FirstCategoryId":"97","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/vec.70020","RegionNum":3,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"VETERINARY SCIENCES","Score":null,"Total":0}
Associations Among Various Physical Parameters and the Axillary-to-Rectal Temperature Difference in Dogs and Cats
Objective
To compare the measurement of axillary temperature (AT) with rectal temperature (RT) in dogs and cats and to identify the influence of physical parameters on the difference between their measurement using a veterinary-specific thermometer.
Design
Prospective study (2022–2024).
Setting
University teaching hospital.
Animals
A total of 106 dogs and 101 cats aged ≥4 months.
Interventions
Body temperatures were measured contemporaneously using a veterinary-specific, calibrated, dual-thermistor axillary thermometer (AT) and a calibrated medical thermometer (RT). Data were evaluated for bias as a whole set and after stratification based on various physical parameters (species, body weight, haircoat length, body condition score [BCS]). A machine learning (ML) model was subsequently applied, and bias was reevaluated.
Measurements and Main Results
A Bland–Altman analysis comparing the measurement of AT with RT showed a bias of 1.01°C (1.82°F) and 95% limits of agreement (LOA) −0.66°C to 2.68°C (−1.19°F to 4.83°F) in dogs and a bias of 0.23°C (0.42°F) and 95% LOA of −2.61°C to 3.08°C (−4.71°F to 5.55°F) in cats. Animals weighing <10 kg had a bias of 0.33°C (95% LOA: −2.29°C to 2.96°C), animals with BCS <5 had a bias of 0.09°C (95% LOA: −2.92°C to 3.11°C), and those with shorter haircoats had a bias of 0.49°C (95% LOA: −2.03°C to 3.00°C). The ML model overestimated RT when using AT and physical parameters in its algorithm (dogs: −0.57°C [95% LOA: −2.18°C to 0.99°C]; cats −0.84°C [95% LOA: −2.62°C to 0.93°C]).
Conclusions
Animals weighing <10 kg, with lower BCS, and shorter haircoats had less bias when AT was compared with RT. Cats exhibited a lower bias between AT and RT than dogs. ML models can be programmed to account for various physical characteristics, improving the predictive impact of AT for directly measured RT, especially in categories of animals where closer prediction does not already exist.
期刊介绍：
The Journal of Veterinary Emergency and Critical Care’s primary aim is to advance the international clinical standard of care for emergency/critical care patients of all species. The journal’s content is relevant to specialist and non-specialist veterinarians practicing emergency/critical care medicine. The journal achieves it aims by publishing descriptions of unique presentation or management; retrospective and prospective evaluations of prognosis, novel diagnosis, or therapy; translational basic science studies with clinical relevance; in depth reviews of pertinent topics; topical news and letters; and regular themed issues.
The journal is the official publication of the Veterinary Emergency and Critical Care Society, the American College of Veterinary Emergency and Critical Care, the European Veterinary Emergency and Critical Care Society, and the European College of Veterinary Emergency and Critical Care. It is a bimonthly publication with international impact and adheres to currently accepted ethical standards.
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