Association of transition cow health with pregnancy per artificial insemination and pregnancy loss in Holstein cows submitted to a Double-Ovsynch protocol for first service
Bibliographic record
Abstract
This observational study was conducted to evaluate the effect of transition cow health on pregnancy per artificial insemination (P/AI) and pregnancy loss (PL) in cows submitted to a Double-Ovsynch protocol (DO) for first service. Lactating Holstein cows (n = 15,041) from one commercial dairy farm in northern Germany between January 2015 to December 2021 were enrolled into a modified Double-Ovsynch protocol (GnRH, 7 d later PGF 2α , 3 d later GnRH, 7 d later GnRH, 7 d later PGF 2α , 24 h later PGF 2α , 32 h later GnRH, and 16 to 18 h later timed artificial insemination) for first service at 72 ± 3 d in milk. Pregnancy was diagnosed at 32 and 60 d post-AI via transrectal ultrasonography. Pregnancy loss was defined as the proportion of cows diagnosed pregnant 32 d post-artificial insemination that were diagnosed nonpregnant 60 d post-artificial insemination. Health-related events (i.e., milk fever [MF], hyperketonemia [KET], retained fetal membranes [RFM], metritis, mastitis, left displaced abomasum [LDA]) were assessed by farm personnel using standard operating procedures. Multivariable logistic regression was used for testing potential associations between transition cow health event occurrence and outcome variables, including P/AI and PL. Three separate models were built for cows in first lactation, second lactation, and ≥third lactation. Overall, 20.0% (885/4,430), 34.9% (1,391/3,989), and 53.9% (3,570/6,622) of cows had at least one transition cow health event for first, second, and ≥third lactations, respectively. The most prevalent transition cow health event for first-lactation cows was metritis (10.7%; [473/4,430]), whereas second-lactation cows suffered mostly from mastitis (16.6%; [664/3,989] and KET (16.6%; [661/3,989]), and cows with ≥third lactations were mostly affected by KET (33.2%; [2,198/6,622]). We observed a negative association between inflammatory disorders (i.e., RFM, metritis, mastitis) and P/AI in all cows irrespective of parity. Metabolic disorders (i.e., MF, KET, LDA) were negatively associated with P/AI only in multiparous cows. Irrespective of parity, only uterine diseases (i.e., RFM, metritis) were significantly associated with PL. These results show that enrolling cows into a fertility protocol, such as DO, cannot overcome the carryover effects of inflammatory and metabolic disorders on P/AI and PL and highlight the importance of optimizing transition cow health as a prerequisite for achieving high fertility in a DO protocol.
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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 | 0.002 |
| 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) | 0.001 | 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".