Characterization and optimization of visual pen checking criteria to improve bovine respiratory disease treatment outcomes in newly arrived feedlot cattle
Notice bibliographique
Résumé
An observational study took place during the fall and winter months of 2021 and 2022, at commercial feedlots in southern Alberta and northern Saskatchewan (n = 5). The purpose of the study was to identify the clinical signs of bovine respiratory disease (BRD). All calves identified for BRD treatment (n = 163) had rectal temperature (RT), blood lactate (BL), and computer-aided lung auscultation (CALA) score measured at chute-side. The thresholds for case definitions were RT ≥ 40°C, BL concentration ≥ 4 mmol/L and CALA score ≥ 2. Nose secretions (Odds Ratio (OR) = 2.43, P = 0.02) and abnormal ear position (OR = 2.11, P = 0.22) were positively associated when the BRD case definition was based on RT. A BRD case definition that combined RT and BL was positively associated with ear position (OR = 5.65, P = 0.11), nose secretions (OR = 3.02, P = 0.04), and lack of stretching (OR = 2.53, P = 0.68). A case definition based on RT, BL and LA scores was positively associated with nose secretions (OR = 7.70, P = 0.01). Treatment outcomes were split into number of BRD treatments based on after the fact health records. Cattle with 2 treatments were associated with abnormal respiration (OR = 1.99, P = 0.35) and ear position (OR = 1.89, P = 0.28). Three treatments were associated with abnormal respiration (OR = 2.77, P = 0.16) and mouth secretions (OR = 2.59, P = 0.12). Chronic cases of 4 or more treatments were associated with abnormal respiration (OR = 5.86, P = 0.09) and nose secretions (OR = 2.44, P = 0.41). Lastly, BRD death cases were associated with flat tail (OR=3.58, P = 0.08) and mouth secretions (OR = 2.52, P = 0.23). To follow, a survey was distributed throughout western Canada, to find commonalities between different cohorts of pen riders. The questions were organized in three sections: background of respondent, methodology to diagnose BRD, and videos of animals with varying severities of BRD. Most of the respondents were male (67%), 18-30 years old (33%), had an average of 11 years of experience pen checking (Median = 8 yrs, SD = 10 yrs), and received training only at the beginning of career by a colleague (79%). A total of 65% of the pen checkers considered laboured breathing/altered respiration the most important clinical sign for BRD diagnosis, followed by slow moving (52%), body posture/head carriage (51%) and isolation from the herd (40%). The video-based questions consisted of eight 10-sec clips of calves recorded in field conditions while evaluated for BRD. Seven clips corresponded to BRD cases with different symptomatology, and one of the clips was from a control animal (no BRD symptoms). To assess the pen checkers diagnostic accuracy, 5 out of the 8 videos were used to create a score for each respondent depending on whether the pen rider was able to successfully identify BRD symptoms. Out of the 68 respondents that assessed all 5 videos, 12 of them (18%) responded correctly, while 24 (35%) responded to 4 out of 5 videos correctly. Respondents with the greater scores had 0-10 years of experience and were 18-30 years old. The research surrounding the art of pen riding should be continued as it offers an inexpensive, and practical method to prevent BRD in feedlots. Much is still unknown about the inner workings of pen riding and more research and observation is required.
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 | 0,004 | 0,007 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 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 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 ».