Effect of nonclinical Staphylococcus aureus or coagulase-negative staphylococci intramammary infection during the first month of lactation on somatic cell count and milk yield in heifers
Bibliographic record
Abstract
Coagulase-negative staphylococci (CNS) are the most prevalent cause of intramammary infections in heifers around calving, but Staphylococcus aureus should not be ignored because it is also prevalent, contagious, and more likely to persist into lactation. The objective of this study was to determine the effect of a subclinical infection caused by S. aureus or CNS diagnosed during the first month of lactation in heifers on SCC, milk production, and culling risk during the entire first lactation. Data were obtained from a cohort of 50 farms following a mastitis monitoring and control program and subscribing to the animal health record system (DS@HR) through the ambulatory clinic of the Faculté de médecine vétérinaire of the Université de Montréal (St-Hyacinthe, Québec, Canada). This program included routinely collecting a composite milk sample at each farm visit from all recently freshened heifers. A total of 2,273 Holstein heifers were examined. Among the 1,691 heifers meeting the full selection criteria, 90 (5%) were diagnosed with S. aureus, 168 (10%) were diagnosed with CNS, and 153 (9%) were negative (no pathogen isolated). Test-day natural logarithm somatic cell count (lnSCC) was modeled in a repeated measures linear regression model with herd as random effect. The model-adjusted mean lnSCC in S. aureus and CNS groups were significantly higher than in the culture-negative group from 40 to 300 d in milk. At the test-day level, lnSCC in S. aureus and CNS groups were on average 1.2 and 0.6 higher, respectively, than the culture-negative group. A similar model for milk yield showed that mean milk yield was not statistically different between culture groups from 40 to 300 d in milk. The presence of a S. aureus or CNS intramammary infections in the first month of lactation in heifers correlates with future increased SCC over the entire first lactation.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".