A scoping review of the testing of bulk tank milk to detect nonbacterial pathogens or herd exposure to nonbacterial pathogens in dairy cattle
Notice bibliographique
Résumé
In this scoping review, we characterized the literature reporting on the testing of bulk milk samples to detect microorganisms other than bacteria that can cause diseases in dairy cattle, including viruses, helminths, algae, and protozoa. A search strategy was completed by screening databases, conference proceedings, animal health agency websites, disease surveillance program websites, and handbooks of cattle-related diagnostic tests for potentially relevant articles. Two reviewers independently screened articles in English, Portuguese, or Spanish; original studies reporting on the testing of farm-level, unprocessed bulk milk samples for presence of pathogens or specific antibodies against agents other than bacteria that can cause diseases in cows were retained. From all studies, we used spreadsheets to extract relevant information, including pathogen screened, test used, and country of origin of bulk milk samples. Additionally, for studies reporting sufficient data to estimate test characteristics, we extracted detailed information about herd eligibility, testing protocol, and herd-level infection definition. A total of 8,829 records were identified, from which 1,592 were retained and assessed for eligibility, and 306 were included. Bovine viral diarrhea virus, Fasciola hepatica, Ostertagia ostertagi, and bovine herpesvirus 1 were the most frequently screened agents, reported from 107, 45, 45, and 33 studies, respectively. Sensitivity of bulk milk ELISA to detect herds with animals infected by bovine herpesvirus 1 ranged from 2 to 100%, and was affected mostly by antigen selection, cut-off adopted, herd vaccination status, and seroprevalence of lactating cows. Bulk milk ELISA had very high specificity to detect herds free of bovine leukemia virus, and varying sensitivity to detect herds with infected animals, which depended on the within-herd seroprevalence of lactating cattle. As for bovine viral diarrhea virus, in general, the sensitivity of bulk milk ELISA was moderate to high (>80%) when infection status was defined based on presence of persistently infected cattle or a high proportion of seropositive lactating cattle. Nevertheless, bulk milk ELISA was not able to distinguish infected and noninfected herds based on presence of seropositive unvaccinated weanlings. The PCR or quantitative PCR protocols employed had very low sensitivities (<40%) and very high specificities (>95%) to classify bovine viral diarrhea virus infection status of dairy herds. Sensitivity and specificity of bulk milk ELISA to classify herds with regards to presence of F. hepatica- or O. ostertagi-parasitized cattle were generally high and driven mostly by the definition of herd infection status. Conversely, bulk milk ELISA demonstrated varying characteristics to detect herds with or without Dictyocaulus viviparus-parasitized cattle, depending primarily on the antigen selected and presence of cattle with clinical signs of lungworm infection.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,015 | 0,072 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,007 | 0,008 |
| Bibliométrie | 0,024 | 0,021 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».