Occurrence of pregnancy losses within the same lactation in grazing dairy cows
Notice bibliographique
Résumé
The study aimed to describe pregnancy losses within the same lactation in a large commercial dairy herd in Argentina. A retrospective study was completed using 25,019 lactation records from 11,263 cows with at least 1 artificial insemination-declared pregnant (AIDP) by ultrasound at 28 to 42 d post AI. Each AIDP was identified according to the corresponding parity number, which was sequentially numbered and related to a pregnancy number within the same lactation. In each lactation, the uterine health events (UTE), retention of fetal membranes, puerperal metritis, and clinical endometritis, as well as the nonuterine health events (NUTE), clinical mastitis, and clinical lameness were recorded. The health status for each lactation was categorized according to the site of inflammation, such as healthy cows, cows with UTE, cows with NUTE records, and cows with both UTE and NUTE. Pregnancy loss was defined by: (1) detection of heat with blue paint rubbed off after having been previously diagnosed pregnant and subsequently diagnosed open by ultrasound at the next herd visit 14 d later; (2) observed abortion; or (3) diagnosis open by ultrasound pregnancy diagnosis 5 mo after AI of pregnancy to reconfirm pregnancy status. The occurrence of pregnancy loss was reported for the whole study period and 21-d periods. The risk of pregnancy loss was analyzed using a Cox proportional hazards model that included parity number, AIDP, season, health status, DIM to AIDP, and daily milk production to AIDP as covariates. Herd persistence was used to assess the risk of cows leaving the herd before the next lactation due to pregnancy loss, with the last pregnancy within each lactation, parity number, and pregnancy loss as covariates. The occurrence of pregnancy loss was 22.5%; the occurrence of pregnancy loss per 21 d was 3.7%. The median day of gestation and median DIM for the first, second, and third pregnancy losses were 98, 108, 121 d and 224, 394, and 552 d, respectively. Cows with UTE diseases had a higher hazard of pregnancy loss than healthy cows (hazard ratio [HR] 1.24, 95% CI 1.13-1.36); conversely, cows in second or third parity did not have a higher hazard of pregnancy loss than first parity cows (HR 1.03, 95% CI 0.95-1.11; HR 1.00, 95% CI 0.92-1.08; respectively). Similarly, cows with 2 and 3 AIDP had a lower hazard of pregnancy loss than cows with only 1 AIDP (HR 0.90, 95% CI 0.83-0.97; HR 0.92, 95% CI 0.85-0.99, respectively). Cows with AIDP in fall and winter had a lower HR of pregnancy loss than those with AIDP in summer (HR 0.84, 95% CI 0.77-0.91; HR 0.82, 95% CI 0.75-0.89). In conclusion, health events during lactation affect pregnancy loss within the same lactation and herd persistence to the next lactation in dairy cows. The risk of pregnancy losses within a lactation may not increase with parity number and higher AIDP, but conversely, it may increase with higher milk production (HR 1.02, 95% CI 1.01-1.02; 1%/1 kg increase in test-day milk yield closest to the AIDP).
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 | 0,001 | 0,002 |
| 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».