Investigating the within-herd prevalence and risk factors for ketosis in dairy cattle in Ontario as diagnosed by the test-day concentration of β-hydroxybutyrate in milk
Bibliographic record
Abstract
An observational study of 790 to over 3,000 herds was conducted to estimate the within-herd prevalence and cow-level risk factors for ketosis in dairy cattle in herds that participate in a Dairy Herd Improvement Association (DHIA) program. Ketosis or hyperketolactia (KET) was diagnosed as milk β-hydroxybutyrate ≥0.15 mmol/L at first DHIA test when tested within the first 30 d in milk. Seven hundred ninety-five herds providing at least 61 first milk tests from June 2014 to December 2015 were used to estimate the provincial within-herd prevalence of KET. All herds on DHIA in Ontario (n = 3,042) were used to construct cow-level multilevel logistic regression models to investigate the association of DHIA collected variables with the odds of KET at first DHIA milk test. Primiparous and multiparous animals were modeled independently. The cow-level KET prevalence in Ontario was 21%, with an average within-herd prevalence of 21% (standard deviation = 10.6) for dairy herds enrolled in a DHIA program. The prevalence of KET had a distinct seasonality with the lowest prevalence occurring from July to November. Automatic milking systems (AMS) were associated with increased within-herd prevalence, as well as increased odds of KET in multiparous animals at first test (odds ratio: 1.45; 95% confidence interval: 1.30 to 1.63). Jersey cattle had over 1.46 times higher odds of KET than Holstein cattle. Milk fat yield ≥1.12 kg/d at the last test of the previous lactation was associated with decreased odds of KET in the current lactation (odds ratio: 0.56; 95% confidence interval: 0.53 to 0.59). Increased days dry and longer calving intervals, for multiparous animals, and older age at first calving for primiparous animals increased the odds of KET at first test. This study confirms previous findings that increased days dry, longer calving intervals, and increased age at first calving are associated with increased odds of KET and is the first report of increased KET in herds with AMS and in relation to milk fat yield at the final test of the previous lactation. Feeding management on AMS herds likely contributes to the increased prevalence of KET and further work is required to investigate modifications to current management to minimize risk. Milk fat yield during the previous lactation may be representative of energy partitioning.
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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 | 0.002 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".