Monitoring Subclinical Ketosis Using Milk Strip Test and Control Chart in Dairy Herds
Bibliographic record
Abstract
Subclinical ketosis (SCK), based on a serum betahydroxybutyrate (BHB) concentration ≥1400 μmol/L (14.4 mg/dL), is a metabolic disease that has been associated with reduced milk production and increased risk of other metabolic disorders. Duffield et al (2001) have reported a median prevalence of SCK of 41% among 25 dairy herds, and a within-herd prevalence ranging between 8 and 80%. A milk strip cow-side test (Keto-Test, Elanco Animal Health, Guelph, Ontario) has been shown to have a sensitivity of 91% and a specificity of 74% to detect SCK in dairy cows when using a cut-off value of 100 μmol/L of BHB (Osborne et al, 2002). Statistical Process Control (SPC) and the use of Control Charts are tools that can be used in order to monitor health status in production medicine programs. Our objectives are to provide an overview of the SPC and Control Charts and to present a practical application of this tool using milk Keto-Test to monitor SCK in dairy herds.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".