1234 Investigating the within-herd prevalence and risk factors of hyperketonemia of dairy cattle in Ontario as diagnosed by the test-day concentration of milk β-hydroxybutyrate
Bibliographic record
Abstract
A large-scale observational study was conducted to estimate the within-herd prevalence and cow-level risk factors of hyperketonemia (HK) in dairy herds in Ontario that participate in a dairy herd improvement association (DHIA) program. Hyperketonemia was diagnosed as milk β-hydroxybutyrate ≥ 0.15 mmol/L (Ketoscreen test, MilkoScan FT600, Foss Analytical A/S, Hillerød, Denmark) at first DHIA test within the first 30 d in milk (DIM). Eight hundred and thirteen herds providing at least 61 first milk tests from June 2014 to December 2015 and were used to estimate the provincial within-herd prevalence with 95% confidence, 80% power and precision of 10%. All herds on DHIA in Ontario (n = 3,042) were used to construct multi-level logistic regression models to investigate the association of commonly measured variables with the odds of HK at first DHIA milk test at the cow-level. The overall HK prevalence in Ontario was 22% of cows at first test, with an average within-herd prevalence of 21% (SD = 10.6). The prevalence of HK had a distinct seasonality with the lowest prevalence occurring from July to November. Herds with automatic milking systems (AMS) (11%, n = 92) had higher within-herd prevalence than all other herds, as well as increased odds of HK in multiparous animals at first test (OR: 1.46; CI95: 1.30 to 1.63). This association requires further study of causal factors. Both primiparous and multiparous Jersey cattle had 1.4 times higher odds of HK than Holstein cattle. After controlling for breed, a milk yield > 26 kg and milk fat > 4.8% at the last milk test of the previous lactation were associated with decreased odds of HK in the current lactation (ORyield: 0.55; ORfat%: 0.83). Increased days dry and longer calving intervals, for multiparous animals, and older age at first calving for primiparous animals increased the odds of hyperketonemia at first test. This is the first report of associations of AMS, and milk yield and components late in the previous lactation with increased HK.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".