Bayesian assessment of diagnostic accuracy of a commercial borescope and of trimming chute exams for diagnosing digital dermatitis in dairy cows
Notice bibliographique
Résumé
Digital dermatitis (DD) is a worldwide infectious disease of cattle that causes lameness, discomfort, and economic losses. The reference standard test to diagnose DD is visual observation in a trimming chute, which cannot be practically performed daily on dairy farms. Moreover, some lesion misclassification may occur using this standard diagnostic method. The possibility of misclassification makes the use of a trimming chute debatable as a perfect reference standard test. The objective of this study was, therefore, to assess the diagnostic accuracy of a commercial borescope and trimming chute exam. The accuracy (sensitivity and specificity) of the tests and DD prevalence were investigated using Bayesian latent class analyses. Our hypothesis was that a commercial borescope can be routinely used to diagnose DD in a milking parlor without previous feet cleaning. A cross-sectional study was performed in a freestall facility. The lesions were scored (M0, M1, M2, M3, M4, M4.1) in the milking parlor with a borescope, followed by an examination in the trimming chute 48 to 72 h after the borescope exam. A total of 870 hind feet were scored during 2 sessions of trimming chute exams and borescope exams in the milking parlor. The data were analyzed in 2 ways. First, data were dichotomized into DD lesions (M1, M2, M3, M4, M4.1) and absence of DD lesions (M0). Second, data were dichotomized into active DD lesions (M1, M2, M4.1) and inactive lesions (M0, M3, M4). A Bayesian latent class model allowing for conditional dependence between tests was used to estimate tests' accuracy, likelihood ratio, and DD prevalence. When the data were dichotomized into DD lesions (M1-M4.1) versus absence of DD (M0) lesions, the sensitivity and specificity of the borescope was 55% [95% credible interval (CrI) 40-71%] and 81% (95% CrI 75-88%). The sensitivity of trimming chute exams was 79% (95% CrI 68-88%), and specificity was 80% (95% CrI 71-89%). When the data were dichotomized into active lesions (M1, M2, M4.1) versus inactive lesions or absence of lesions (M3, M4, M0), the sensitivity and specificity of the borescope were, respectively, 32% (95% CrI 13-58%) and 91% (95% CrI 88-95%). The sensitivity and specificity of trimming chute exams were 91% (95% CrI 81-97%) and 81% (95% CrI 75-89%), respectively. In conclusion, it is possible to use the borescope in the milking parlor without cleaning the feet to monitor prevalence of DD lesions. However, an isolated borescope examination, especially for diagnosing active DD lesions, has low sensitivity for use as a surveillance method. For such use, the sensitivity could be improved by repeating the borescope exam on a regular basis.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».