犬と猫における様々な物理的パラメータと頭から直腸までの温度差の関連
Cristian Martinez Alvarez1, Britt A L Thevelein1, Katie M Hodges1
1Department of Small Animal Medicine and Surgery, College of Veterinary Medicine, University of Georgia, Athens, Georgia, USA.
まとめ
犬と猫の頭温度 (AT) の測定は,体調スコアが低く,毛皮が短い小型の動物では偏差が少ないことを示した. 機械学習モデルは,直腸温度 (RT) を予測するATの精度を改善することができます.
科学分野:
- 獣医学
- 動物生理学
- 比較温度調節
背景:
- 正確な体温測定は動物の健康診断とモニタリングに不可欠です.
- 直腸温度 (RT) はゴールドスタンダードと考えられていますが,頭温度 (AT) はより侵襲的な代替手段です.
- ATの精度に影響を与える要因を理解することは,その臨床適用にとって不可欠です.
研究 の 目的:
- 犬や猫の頭温度 (AT) と直腸温度 (RT) を比較する.
- 物理的なパラメータ (種,体重,毛皮,身体状態スコア) がATとRTの差異に与える影響を調査する.
- ATベースのRT予測の改善における機械学習 (ML) モデルの有用性を評価する.
主な方法:
- 2022年から2024年にかけての大学病院での将来性研究.
- 4ヶ月以上の犬106匹と猫101匹を対象とした.
- 獣医専用および医療用温度計を用いた同時のATおよびRT測定,次にBland-Altman分析およびMLモデリングを行う.
主要な成果:
- 犬はATとRTの間に1. 01°Cのバイアスを示し,猫は0. 23°Cのバイアスを示した.
- 体重が10kg未満で,体質スコアが5以下で,毛皮が短い動物では偏差が低かった.
- MLモデルはRTのAT予測を改善し,犬の偏差は- 0. 57°C,猫の偏差は- 0. 84°Cであった.
結論:
- 犬や猫の直腸の体温の測定は,直腸の体温の測定よりも有効で,侵襲的な方法ではありません.
- 体重,体調スコア,毛皮の長さはATの精度に影響します.
- 機械学習アルゴリズムは,直腸の温度測定の予測精度を高めることができます.
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