Evaluation of prepartum serum cholesterol and fatty acids concentrations as predictors of postpartum retention of the placenta in dairy cows
Bibliographic record
Abstract
OBJECTIVE: To identify serum biochemical and hematologic variables, as measured in the week before parturition, that predict postpartum retention of the placenta (RP) in dairy cows. DESIGN: Retrospective cohort study. ANIMALS: 1,038 cows in 20 commercial dairy herds. PROCEDURES: Serum concentrations of fatty acids (FAs), beta-hydroxybutyrate, cholesterol, glucose, urea, and calcium and blood leukocyte, neutrophil, lymphocyte, monocyte, and eosinophil counts were determined. These variables were evaluated for an association with development of RP by use of a multivariate logistic regression model. Parity, season of parturition, existence of twins or dystocia, body condition score, and vitamin E treatment were included in the model as covariates. RESULTS: High serum concentrations of cholesterol and FAs were associated with an increased odds of RP. There was a 5% relative increase in the odds of RP for each 0.1 mmol/L increase in cholesterol or FAs concentration in the week before parturition. Season of parturition and twinning were also identified as risk factors. CONCLUSIONS AND CLINICAL RELEVANCE: These associations indicated that prepartum energy metabolism contributes to the development of RP. Serum concentrations of cholesterol and FAs may be useful to identify cows with a metabolic abnormality or energy imbalance that might predispose them to RP and should be interpreted in conjunction with clinical risk factors such as twinning, dystocia, or parturient paresis.
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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) | 0.001 | 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".