Risk factors identified on arrival associated with morbidity and mortality at a grain-fed veal facility: A prospective, single-cohort study
Notice bibliographique
Résumé
High levels of morbidity, mortality, and antimicrobial use are common in the veal industry. For the sustainability of the industry, it is important to address these challenges and determine factors that can be used to improve animal health and welfare. The objectives of this prospective observational cohort study were to describe the health status of calves on arrival at a grain-fed veal facility and determine characteristics that were associated with morbidity and mortality. On arrival, the calves were assessed for health abnormalities, weighed, measured for hip height and length from withers to lumbosacral junction, and had blood collected to determine serum total protein level. Body mass index (BMI) was calculated using weight on arrival divided by the sum of the calf's length from withers to lumbosacral junction and hip height. The calves were observed for 77 d after arrival, during which the producer was responsible for recording all antibiotic treatments and mortality. We built Cox proportional hazard models for morbidity and mortality variables. We created 2 morbidity models: 1 for calves that were treated <21 d after arrival at the facility, and 1 for calves that were treated during the 11 wk at the facility. From January to December 2017, 998 calves were evaluated on arrival at the veal rearing facility. A large proportion (68%) were treated with antibiotics in the first 21 d after arrival. The only explanatory variable in the final <21 d morbidity model was BMI: calves with a higher BMI had a lower hazard of being treated for disease in the first 21 d after arrival. A total of 872 calves (87%) were treated at least once over the 11-wk housing period. A calf arriving with a BMI >371.46 g/cm or a serum total protein between 5.8 and 6.2 g/dL had a lower level of morbidity during the observation period. Over the 11-wk housing period, 74 calves (7.5%) died. Calves that arrived at the facility with a fecal score of 2 or a rectal temperature between 38.9 and 39.1°C had a higher hazard of dying; calves that had a BMI of >371 g/cm were less likely to die than calves that had a BMI <330 g/cm. These results point to the importance of assessing calves on arrival at a veal facility as a measure of identifying those at high risk for morbidity and mortality.
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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 | 0,002 | 0,001 |
| 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 | 0,001 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 ».