Characterizing the literature surrounding transportation of young dairy calves: A scoping review
Notice bibliographique
Résumé
Transportation is a stressful event for cattle, as it may involve various handling practices, commingling, deprivation of food and water, and fluctuating temperatures. Calves are particularly susceptible to these stressors because their physiological and immune systems are still developing. There has been no formal synthesis of the scientific literature evaluating the effect of transportation on young dairy calf health and performance; the aim of this scoping review is to describe and characterize this body of work. We targeted both descriptive and analytic studies examining transport of calves, including listing how the effect of transport has been evaluated. Eight databases were searched for relevant articles with eligible studies being primary research articles investigating transportation of calves of either sex who were younger than 60 d of age or weighed less than 100 kg. Two reviewers independently screened the title and abstracts of 6,859 articles with 361 potentially relevant articles screened at full text. Of these, 46 were relevant and had data extracted. Articles reporting study location were conducted in the United States (n = 5), Australia (n = 3), Japan (n = 3), and New Zealand (n = 3). Common transport-related variables evaluated included time in transit (n = 13), distance of transportation (n = 8), vehicle-related factors (n = 8), and age at time of transportation (n = 4). Outcome measures varied greatly, including blood parameters (n = 28), health assessments (n = 20), weight (n = 17), behavioral metrics (n = 14), mortality (n = 7), feed intake following transportation (n = 4), salivary cortisol concentrations (n = 3), morbidity (n = 3), and isolation of Salmonella Dublin in fecal samples (n = 2). Outcome parameters were measured during transport or ranged from immediately after to one year following transportation. As the transport-related risk factors and outcomes measured assessed varied widely between studies, future quantitative synthesis (e.g., meta-analysis) in this area may be limited. Several knowledge gaps were identified, including methods to prepare calves for transportation, such as improving nutrition, administering medication, or transporting calves at an older age or weight. Further research could also focus on consistent and clear reporting of key items related to study conduct and analysis, as well as the development of a core outcome set for calf transport studies.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,003 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 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 ».