Association of uterine health in the first lactation with transition cow health and reproductive performance in the second lactation of Holstein dairy cows
Bibliographic record
Abstract
The objective of this study was to evaluate the effects of puerperal metritis (PM) diagnosed and treated during the early postpartum period of the first lactation on transition cow health, milk production, reproduction, and culling of dairy cows in their second lactation. Diagnosis of PM was based on fetid watery red-brown uterine discharge and rectal temperature above 39.5°C. Two farms were enrolled in this retrospective observational cohort study (farms A and B). In both farms, the following diseases were recorded during the first 30 DIM in lactation 1 and 2: clinical hypocalcemia (CH), retained fetal membrane (RFM), PM, hyperketonemia (KET), left displaced abomasum (LDA), and clinical mastitis (MAST). Statistical analyses were performed using SPSS for Windows separately for each farm. Linear and logistic regression models were used for continuous (e.g., milk yield) and binary (e.g., disease, pregnancy per AI, pregnancy loss) outcomes, respectively. Cox proportional hazard regression models were calculated to model the time to event outcomes for culling or death during the first 60 DIM and for pregnancy within 250 d of the second lactation. The initial models contained the following variables: year of calving, month of calving, calving ease, stillbirth, twins, days open in lactation 1, 305-d milk yield in lactation 1, PM in lactation 1, and PM in lactation 2 as explanatory variables. A total of 4,834 cows (farm A) and 4,238 cows (farm B) in the second lactation were considered for statistical analyses. On farm A, the incidence of PM in lactations 1 and 2 were 20.1% and 11.2%, respectively. On farm B, the incidence of PM in lactations 1 and 2 were 14.4% and 8.5%, respectively. On both farms, cows with PM in their first lactation had greater odds for RFM and PM in their second lactation, whereas there was no association of PM in the first lactation with any other nonuterine diseases (i.e., CH, KET, LDA, and MAST) in the second lactation. Cows with PM in lactation 2 had reduced milk yield. The reduction in milk yield in second lactation was greater for cows that already experienced PM in lactation 1. On farm A, cows with PM in their first lactation had a greater hazard for culling within 60 DIM of the second lactation; however, the same association was not present on farm B. Cows with PM in lactation 1 had reduced pregnancy per AI at first service in the second lactation only on farm B. Cows with PM in lactation 2 had reduced pregnancy per AI at first service in the second lactation on both farms. Pregnancy loss in lactation 2 was only associated with PM in lactation 2 but not with PM in lactation 1. On both farms, cows had a reduced hazard for pregnancy in their second lactation within 250 DIM when they experienced PM in either lactation. In conclusion, PM in the first lactation had long-lasting negative consequences (i.e., risk of uterine disease and lower reproductive performance) for cows in their next lactation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".