The association of serum metabolites with clinical disease during the transition period
Bibliographic record
Abstract
The objective of this observational field study was to validate the relationship of serum concentrations of nonesterified fatty acids (NEFA), β-hydroxybutyrate (BHBA), and calcium with disease in early lactation across different management systems. Fifty-five Holstein freestall dairy herds located across the United States and Canada were selected and visited weekly for blood sample collection from 2,365 cows. Only diseases that were consistently recorded across herds and blood samples collected before the disease occurred were considered. Metabolite concentrations in serum in wk -1 relative to calving were considered as predictors of retained placenta (RP) and metritis, and metabolite concentrations in serum in wk -1 and wk +1 relative to calving were considered as predictors of displaced abomasum (DA). For each disease, each metabolite, and week of sampling in the case of DA, a critical threshold was calculated based on the highest combined sensitivity and specificity and used to categorize the serum concentrations into high and low risk categories. Multivariable logistic regression models were built for each disease of interest and week of sampling, considering cow as the experimental unit and herd as a random effect. Cows with precalving serum NEFA concentrations ≥ 0.3 mEq/L were more likely to develop RP [odds ratio (OR) = 1.8; 95% confidence interval (CI) = 1.3 to 2.6] and metritis (OR = 1.8; 95% CI = 1.5 to 2.9) after calving than cows with lower NEFA concentrations. Precalving NEFA ≥ 0.5 mEq/L (OR = 2.4; 95% CI = 1.5 to 3.7), postcalving NEFA ≥ 1.0 mEq/L (OR = 2.7; 95% CI = 1.7 to 4.4), and postcalving calcium ≤ 2.2 mmol/L (OR = 3.1; 95% CI = 1.9 to 5.0) were associated with subsequent risk of DA. In conclusion, elevated serum NEFA concentrations within 1 wk before calving were associated with increased risk of RP, metritis, and DA after calving. Serum NEFA and calcium concentrations in the 2 wk around calving in combination were associated with the risk of DA.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".