Nonesterified fatty acids during the dry period and their association with peripartum disorders, culling, and pregnancy in dairy cows
Bibliographic record
Abstract
The objective of this ambidirectional observational cohort study was to explore how nonesterified fatty acids (NEFA) 22 to 35 d before calving were related to NEFA 1 to 14 d before calving and to determine a threshold that could be used to identify cows at risk of poor postpartum health. We enrolled 855 dairy cows from 46 herds, 362 prospectively and 493 retrospectively. The NEFA concentrations were measured during the far-off period (foNEFA; 3 to 5 wk before calving) and in the close-up period (cuNEFA; up to 2 wk before calving), and postpartum infectious and metabolic disorders, reproduction success, and culling were recorded. Using a split dataset, we (1) determined a threshold maximizing the sum of sensitivity and specificity to identify peripartum conditions by classifying elevated NEFA and (2) assessed the associations between elevated NEFA and altered health and reproduction. The associations were expressed as the odds ratio (OR) and the 95% Bayesian credible interval (BCI). The concentration of foNEFA varied from 60 to 700 µmol/L (median = 149), and a threshold of ≥160 µmol/L was identified. Cows with elevated foNEFA had greater odds to have elevated cuNEFA (OR = 183, 95% BCI = 52.1-458), hyperketonemia (OR = 2.0, 95% BCI = 1.0-3.6), displaced abomasum (OR = 12.3, 95% BCI = 1.6-45.8), metritis (OR = 9.4, 95% BCI = 1.3-36.0), and clinical mastitis (OR = 5.8, 95% BCI = 1.9-12.1) than cows below the threshold. Our results suggest that foNEFA, using a threshold of ≥160 µmol/L, could be used by veterinarians as a monitoring or investigating tool to assess cows' negative energy balance before calving, even earlier than 2 wk prepartum. This monitoring could be used to implement early corrective actions to prevent the effect of negative energy balance on reproduction and peripartum health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".