Quarter‐milking‐, quarter‐, udder‐ and lactation‐level risk factors and indicators for clinical mastitis during lactation in pasture‐fed dairy cows managed in an automatic milking system
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Notice bibliographique
Résumé
OBJECTIVES: To identify risk factors and indicators at quarter-milking and other levels for clinical mastitis during lactation in pasture-fed cows managed in an automatic milking system. DESIGN: A retrospective nested case-control study using data collected over a 31-month period. A total of 803,948 quarter-milkings from 249 cows were available for analysis, of which 245 were selected as clinical mastitis quarter-milking cases and 2450 unaffected quarter-milkings were selected as controls. Most of the diagnoses of clinical mastitis had been made after clinical assessments of quarters following automated alerts by the milking system. Potential risk factors and indicators were assessed using univariable and multivariable multilevel logistic models. RESULTS: Quarters that are milked infrequently and have low yield, fast peak milk flow rates, blood in the milk and/or elevated milk conductivity are at increased risk of clinical mastitis. Quarters were also at increased risk between days 10 and 29 of lactation, and during higher parity lactations. Milk fat and protein concentrations and the fat:protein ratio were not significantly associated with the development of clinical mastitis. Neither quarter position (fore or rear) nor side (left or right) was associated with clinical mastitis occurrence. Quarters that had been clinically affected more than 30 days previous in the current lactation were not at increased risk of redeveloping clinical mastitis. CONCLUSIONS: Under the study conditions, the risk of clinical mastitis increased for quarter-milkings in quarters that were milked less frequently in the previous 7 days, were low-yielding, had fast peak milk flow rates and had more milkings in which blood was detected in milk and/or the milk conductivity was elevated. Risk was also increased in days 10-29 of lactation and in higher parity lactations.
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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 | 0,000 |
| 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 découle