Evaluating case definitions of respiratory disease in dairy calves: A scoping review
Notice bibliographique
Résumé
Bovine respiratory disease (BRD) is one of the most common diseases observed in dairy calves in both the pre- and postweaning periods. Despite its common occurrence, no formal synthesis (e.g., scoping review) of the scientific literature has been conducted surrounding diagnosing BRD in dairy calves. As such, the objective of this scoping review was to describe what case definitions for diagnosing BRD in dairy calves have been used in research. An initial search of peer-reviewed journals and the gray literature yielded 12,001 unique articles, of which 307 records containing 358 case definitions were included. Seven categories of diagnostic methods were used to categorize case definitions: clinical scoring systems alone were the most used diagnostic method (n = 148, 41%); followed by physical exam parameters alone (i.e., the study did not provide a detailed description of how the calf was assessed for each component of the physical exam [i.e., no description or scoring options reported]), n = 91, 25%); physical exam parameters in combination with other diagnostic methods (n = 40, 11%); thoracic ultrasound alone (n = 31, 9%); clinical scoring system in combination with other diagnostic methods (n = 23, 7%); clinical scoring system in combination with thoracic ultrasound (n = 10, 3%); and other diagnostic methods (n = 15, 4%). Only two-thirds of publications provided a citation for their case definition (n = 230, 64%), of which the Wisconsin clinical scoring system was most referenced (n = 100, 43%), followed by the California clinical scoring system (n = 19, 8%). An attempt was made to compare entire case definitions within each diagnostic method; however, they displayed substantial diversity, which limited the ability to compare them at this level. Instead, counts of individual factors within each case definition (e.g., individual clinical signs) were reported for each diagnostic method. From all case definitions across the 7 diagnostic method categories (n = 358), common factors assessed included cough (n = 224, 63%), nasal discharge (n = 213, 60%), and body temperature (n = 142, 54%). Across all case definitions, additional parameters such as validity were frequently not reported; only 4 (1.1%) were referred to as validated, 9 (2.5%) were being validated in the study, and 28 (7.8%) modified existing case definitions. Additionally, assessment of severity of disease when present (e.g., mild, moderate or severe) was frequently not reported (n = 336, 93.9%). Further, 106 (29.0%) reported assessments of accuracy, 66 (18.4%) of which were reports of qualitative oversight (e.g., training of assessors), and 40 (11.1%) of which were related to a quantitative assessment of accuracy (e.g., sensitivity and specificity). Parameters surrounding conducting the assessment were also extracted, for example how often it was conducted (most common: at least daily, n = 120, 33.5%) and who conducted it (most common: information not reported, n = 144, 40.2%). The findings of this scoping review highlighted the need for a validated, standardized set of criteria for BRD diagnosis in dairy calves, achievable through following the methodology of developing a core outcome set.
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 | 0,030 | 0,130 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,005 | 0,005 |
| Bibliométrie | 0,032 | 0,018 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,003 | 0,003 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».