Occurrence of pregnancy losses within the same lactation in grazing dairy cows
Bibliographic record
Abstract
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).
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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 | 0.001 | 0.001 |
| 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".