A Multi-faceted Approach to the Exploration of Ketosis in Dairy Cattle: Detection, Treatment & Risk Factors
Bibliographic record
Abstract
The aim of this thesis was to evaluate the diagnostic accuracy for ketosis (KET) detection methods, to estimate the prevalence of KET in Ontario, Canada and identify risk factors for increased within-herd prevalence and increased odds of KET at the cow-level. Finally, the benefit of including a glucocorticoid with propylene glycol in KET treatment was evaluated. A diagnostic test accuracy systematic review and meta- analysis was conducted for on-farm methods for the diagnosis of KET. The Precision Xtra handheld device that measures blood beta-hydroxybutyrate (BHBA) had the highest combined sensitivity (95%) and specificity (98%) of the methods evaluated. The ability of the Precision Xtra to accurately detect elevated prepartum blood BHBA at concentrations implicated in postpartum disease (0.6 to 0.8 mmol/L) was investigated. The device was moderately well correlated (0.77) with laboratory serum BHBA values, was in substantial agreement with laboratory NEFA identification of at-risk animals (κ = 0.64) and was associated with 2.2 fold greater odds of KET postpartum when prepartum BHBA ≥ 0.6 mmol/L. The within-herd and cow-level prevalence of KET was estimated to be 21% based on early lactation milk BHBA collected from herds participating in a dairy herd improvement program in Ontario, Canada. A longer calving interval, a longer dry period, being tested before 14 days in milk and having lower milk yield and fat percentages at the last test of the previous lactation was associated with increased odds of KET in multiparous animals. Increased odds of KET in primiparous animals were associated with being older at first calving. Being a Jersey increased odds of KET in all parities. Other factors associated with increased KET risk were calving in the spring and being from a herd with an automatic milking system. A randomized controlled trial evaluated the use of dexamethasone as an adjunctive therapy to propylene glycol treatment for KET. The addition of dexamethasone was associated with lower odds of KET in the week following treatment when blood BHBA was between 1.2 and 1.5 mmol/L at diagnosis, but the odds of KET after treatment were increased for the dexamethasone group when blood BHBA ≥ 3.2 mmol/L at diagnosis.
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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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".