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利用泌乳停止时收集的数据预测奶牛随后泌乳期 sole 溃疡和白线病的发病率。

Use of data collected at cessation of lactation to predict incidence of sole ulcers and white line disease during the subsequent lactation in dairy cows.

作者信息

Machado Vinícius S, Caixeta Luciano S, Bicalho Rodrigo C

机构信息

Department of Population Medicine and Diagnostic Sciences, College of Veterinary Medicine, Cornell University, Ithaca, NY 14853, USA.

出版信息

Am J Vet Res. 2011 Oct;72(10):1338-43. doi: 10.2460/ajvr.72.10.1338.

DOI:10.2460/ajvr.72.10.1338
PMID:21962276
Abstract

OBJECTIVE

To develop a parsimonious statistical model to predict incidence of lameness in the subsequent lactation by use of data collected at cessation of lactation in dairy cows.

ANIMALS

574 cows.

PROCEDURES

At cessation of lactation during hoof trimming, body condition score (BCS), visual locomotion score, digital cushion thickness (DCT), and digital lesions were assessed.

RESULTS

140 (24%) cows were treated for claw horn disruption lesions (CHDLs) at cessation of lactation (114 with sole ulcers [pododermatitis circumscripta] and 26 with white line disease). The BCS was highly associated with DCT. Cows with CHDLs at cessation of lactation had significantly lower DCT, compared with other cows. All 3 logistic regression models predicted the incidence of CHDLs in the subsequent lactation with good accuracy; the area under the receiver operating characteristic curves was 0.76, 0.76, and 0.77 for the first, second, and third logistic regression models, respectively.

CONCLUSION AND CLINICAL RELEVANCE

Evaluation of 3 logistic regression models indicated that lameness could be predicted with good accuracy by use of all 3. The ability to predict lameness will facilitate the implementation of lameness prevention strategies by targeting specific cows.

摘要

目的

利用奶牛泌乳期结束时收集的数据,建立一个简约的统计模型,以预测后续泌乳期的跛足发生率。

动物

574头奶牛。

方法

在蹄部修剪时泌乳期结束时,评估体况评分(BCS)、视觉运动评分、趾垫厚度(DCT)和趾部病变。

结果

140头(24%)奶牛在泌乳期结束时因蹄角质破坏损伤(CHDLs)接受治疗(114头患有蹄底溃疡[局限性蹄皮炎],26头患有白线病)。BCS与DCT高度相关。与其他奶牛相比,泌乳期结束时患有CHDLs的奶牛DCT显著更低。所有3个逻辑回归模型均能较好地预测后续泌乳期CHDLs的发生率;第一个、第二个和第三个逻辑回归模型的受试者工作特征曲线下面积分别为0.76、0.76和0.77。

结论及临床意义

对3个逻辑回归模型的评估表明,使用所有3个模型都能较好地预测跛足。预测跛足的能力将有助于通过针对特定奶牛实施跛足预防策略。

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