Usefulness of differential somatic cell count for udder health monitoring: Identifying referential values for differential somatic cell count in healthy quarters and quarters with subclinical mastitis
Bibliographic record
Abstract
Mastitis, an inflammation of the udder primarily caused by an IMI, is one of the most common diseases in dairy cattle. Somatic cell count has been widely used as an indicator of udder inflammation, assisting in the detection of subclinical mastitis. More recently, differential SCC (DSCC), which represents the combined proportion of lymphocytes and polymorphonuclear leukocytes, has become available for routine dairy milk screening, although it was not yet widely studied. Therefore, the objective of this study was to assess and compare the usefulness of quarter-level SCS or DSCC to predict the probability of subclinical mastitis. Additionally, our goals included estimating the sensitivity (Se) and specificity (Sp) of SCS and DSCC across all potential cut-off values. The current study was an observational study conducted on commercial dairy farms. Five dairy herds were selected using a convenience sampling. A Gaussian finite mixture model (GFMM) was applied to investigate the latent quarter subclinical mastitis status with either measurement, SCS or DSCC. Posterior values for SCS and DSCC obtained from the GFMM were used for predictive estimation of the parameters. The estimated SCS distribution for healthy quarters had a mean (SD) of 1.4 (1.3), and, for quarters with subclinical mastitis, it was 4.5 (2.4). For DSCC, the estimated mean was 55.6% (15.2) for healthy quarters, whereas it was 80.4% (6.4) for quarters with subclinical mastitis. The most discriminant cut-off for SCS, as indicated by the Youden index, was 3.0, corresponding to exactly 100,000 cells/mL. At this threshold, the Se and Sp of SCS were 0.73 (95% Bayesian credible interval [BCI]: 0.70-0.77) and 0.90 (95% BCI: 0.89-0.91), respectively. The most discriminant cut-off point for DSCC was 70.0%, with corresponding Se and Sp values of 0.95 (0.93, 0.96) and 0.83 (0.81, 0.85), respectively. For the SCS analysis, we obtained predictive probabilities of subclinical mastitis approaching 0 and 100%, with only a narrow range of SCS results yielding intermediate probabilities. On the other hand, predictive probabilities ranging from 0 to 90% were obtained for DSCC analysis, with a large range of DSCC results presenting intermediate probabilities. Thus, SCS seemed to surpass DSCC for predicting subclinical mastitis. These findings provided a foundation for future studies to further explore and validate the efficacy of GFMM for diagnostic tests yielding quantitative results.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".