Case study: Evaluating quarter and composite milk sampling for detection of subclinical intramammary infections in dairy cattle
Bibliographic record
Abstract
Our objective was to evaluate a 200,000 cells/mL somatic cell count (SCC) cut-point on both the quarter and composite level to determine its effectiveness at identifying subclinical mastitis infections in one commercial dairy herd in Central New York. Milk samples from 107 Holstein cows were used for analysis. All cows were eligible for enrollment provided they had 4 working udder quarters, were >14 and <365 d in milk, and had no clinical mastitis event or treatment with intramammary antibiotics ≤14 d prior to d of sampling. A total of 428 quarter and 107 composite samples from 34 primiparous and 73 multiparous animals were analyzed for total SCC and aerobic culture. Performance of SCC for identification of subclinically infected animals was evaluated against the gold standard aerobic culture. Sensitivities for a 200,000 cells/mL cut-point on both the quarter and composite basis were 28.6%, and specificities were 91.5% and 87.3% on the quarter and composite basis, respectively. Receiver operating characteristic (ROC) curves determined on a quarter basis found the cut-point that optimized the sensitivity and specificity of a positive culture was 32,000 cells/mL, with a sensitivity of 76.2%, a specificity of 62.4%, and an area under the curve (AUC) of 0.73. The ROC curve cut-point that optimized the sensitivity and specificity for the composite samples was 75,000 cells/mL, with a sensitivity of 57.1%, a specificity of 78.9%, and an AUC of 0.67. A large proportion of culture positive primiparous cows (38%) had low SCC (median of 101,000 cells/mL on the quarter and 80,000 cells/mL on the composite level), and therefore, when multiparous cows were examined separately, the cut-point that optimized sensitivity and specificity on the quarter basis increased to 645,000 cells/mL with a corresponding sensitivity of 34.8%, specificity of 97.5%, and AUC of 0.65. On the composite basis, the cut-point based on multiparous cows only was 152,000 cells/mL, with a corresponding sensitivity of 60.0%, and specificity of 82.0%, and an AUC of 0.65. Our data indicate that the 200,000 cells/mL cut-point was inefficient in identifying subclinically infected animals, regardless of whether quarter or composite sampling was used. The low prevalence of subclinical infections as well as the large proportion of minor pathogens, especially in primiparous animals, contributed to this inefficiency. This case study provides evidence that, with continued improvement upon mastitis control and reduction in major mastitis pathogens, blanket cut-points may no longer provide the same diagnostic usefulness as they once did.
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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 | 0.000 |
| 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".