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Enregistrement W6977922360 · doi:10.7939/82038

Using knowledge translation to support the integration of laboratory testing for antimicrobial stewardship in Canadian feedlots

2025· dissertation· en· W6977922360 sur OpenAlexaboutno aff

Notice bibliographique

RevueUniversity of Alberta Library · 2025
Typedissertation
Langueen
DomaineImmunology and Microbiology
ThématiqueMicrobial infections and disease research
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésAntimicrobial stewardshipLivestockStewardship (theology)Antibiotic resistanceMetagenomicsFeedlotAgricultureBovine respiratory diseaseAnimal production

Résumé

récupéré en direct d'OpenAlex

The increased burden of antimicrobial-resistant bacterial infections in humans and animals, antimicrobial resistance genes (ARGs) in the environment, and the potential for their transmission between humans and animals have increased pressure for food animal livestock production to demonstrate antimicrobial stewardship (AMS). Bovine respiratory disease (BRD) is a complex multifactorial disease causing high morbidity and mortality of calves in feedlot production, with antimicrobials playing an important role in management. The use of antimicrobial agents in animal agriculture has been linked to the emergence of AMR in bacterial populations, including those that can infect both animals and humans. To mitigate AMR-associated risk, AMS strategies include development and implementation of diagnostic testing to inform AMU. Laboratory tests have provided life-saving information to detect, characterize, and inform management and treatment decisions for bacterial infections in human and animal health. However, several factors have limited their application in livestock production. My thesis is part of a larger project – Genomic ASSETS (Antimicrobial Stewardships Systems for Evidence-based Treatment Strategies) for Livestock. My research objectives were to 1) synthesize available knowledge in peer-reviewed literature and relevant grey literature for the direct sample application of long-read metagenomic sequencing for diagnosis of bacterial respiratory infections and related antimicrobial resistance genes and to compare long-read, sequencing methods to other molecular diagnostic techniques for nucleic acid detection; 2) identify factors that influence respiratory sample collection from live animals for laboratory testing to inform AMS for BRD management in Canadian feedlot cattle; and 3) explain how the Need-to-Knowledge (NtK) model discovery stages can be applied to designing and using a novel laboratory testing strategy as part of BRD management in western Canadian feedlots. A scoping review resulted in the synthesis of 100 peer-reviewed studies on the direct application of long-read metagenomic sequencing to detect bacterial pathogens and antimicrobial resistance genes (ARGs) in respiratory samples compared to other molecular based laboratory tools. Our review reveals a knowledge gap in research for the direct detection of bacterial respiratory pathogens and ARGs in animals using long-read metagenomic sequencing. However, there is an opportunity to harness new developments to detect multiple pathogens and ARGs on a single sequencing run. Feedlot veterinarians were interviewed to identify factors that influence live animal respiratory sample collection and laboratory testing as part of BRD management in Canadian feedlots. Eight veterinarians were interviewed from Alberta, Saskatchewan, and Ontario, representing practices responsible for about 90% of fed-calf in Canada. This study identified the interconnections between the scientific and ethical lenses by which Canadian feedlot veterinarians view the possible integration of laboratory testing for BRD. Participants were interested in laboratory testing strategies that provide demonstrated animal health and economic benefits for their feedlot clients. They highlighted extensive experience with live animal testing for BRD research and surveillance, but acknowledge that it is not part of current BRD management. Organizational factors such as capacity, coordination, and communication are crucial when considering how a laboratory test could effectively integrate into Canadian feedlots as a support for management of BRD. The complexity of the successful development and sustainable use of new technology as an AMS strategy requires effective and appropriate knowledge translation. The NtK framework integrates three sets of best practices in scientific research, engineering development, and industrial production of technological innovations. The NtK model discovery stage was applied post hoc to the knowledge synthesis phase of Genomic ASSETS to describe knowledge translation in a complex project aimed at developing and implementing a new technology. This project demonstrated how a structured methodology can align research outputs with industry needs to promote uptake of practical solutions in livestock production. This thesis examined both the opportunities and gaps in developing laboratory technologies as part of AMS strategies, and the factors influencing the adoption of these technologies as part of BRD management in Canadian feedlots. These insights and recommendations are applicable not only in Canada but also in global efforts to reduce AMR in food animal production. This work emphasizes the essential role of multi-sectoral communication, collaboration, coordination, and capacity building to support laboratory testing as part of AMS strategies in feedlots. It provides a foundation to promote the successful design, adoption, and implementation of laboratory testing, such as metagenomic technologies in feedlot settings.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,388
Score d'incertitude au seuil0,785

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,052
Tête enseignante GPT0,287
Écart entre enseignants0,235 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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