Evaluation of Three Cow-side Tests for Detection of Subclinical Ketosis in Fresh Cows
Bibliographic record
Abstract
Subclinical ketosis in dairy cattle can lead to economic losses through decreased milk production, decreased reproductive performance, increased risk of displaced abomasum and increased risk of clinical ketosis. For early detection of the disease, there is a need for rapid and accurate diagnostic tests.
 The objective of the study was to evaluate the performance of three cow-side diagnostic tests for the detection of subclinical ketosis in fresh dairy cows, compared to the gold standard serum β-hydroxybutyrate (BHBA). The cow-side tests were: (1) a commonly used test strip detecting acetoacetate in urine (Ketostix, Bayer Corporation, Elkhart, Indiana, USA), (2) a commonly used powder test used on milk, also detecting acetoacetate (KetoCheck, Great States Animal Health, St. Joseph, Missouri, USA), and (3) a milk test strip detecting BHBA (KetoTest, Sanwa Kagaku Kenkyusho Co. Ltd., Nagoya, Japan, distributed by Elanco Animal Health/Provel, Guelph, Ontario, Canada).
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| 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".