Evaluation of an automated milk leukocyte differential test and the California Mastitis Test for detecting intramammary infection in early- and late-lactation quarters and cows
Bibliographic record
Abstract
Study objectives were to (1) describe the diagnostic test characteristics of an automated milk leukocyte differential (MLD) test and the California Mastitis Test (CMT) to identify intramammary infection (IMI) in early- (EL) and late-lactation (LL) quarters and cows when using 3 different approaches to define IMI from milk culture, and (2) describe the repeatability of MLD test results at both the quarter and cow level. Eighty-six EL and 90 LL Holstein cows were sampled from 3 Midwest herds. Quarter milk samples were collected for a cow-side CMT test, milk culture, and MLD testing. Quarter IMI status was defined by 3 methods: culture of a single milk sample, culture of duplicate samples with parallel interpretation, and culture of duplicate samples with serial interpretation. The MLD testing was completed in duplicate within 8 h of sample collection; MLD results (positive/negative) were reported at each possible threshold setting (1-18 for EL; 1-12 for LL) and CMT results (positive/negative) were reported at each possible cut-points (trace, ≥1, ≥2, or 3). We created 2 × 2 tables to compare MLD and CMT results to milk culture, at both the quarter and cow level, when using each of 3 different definitions of IMI as the referent test. Paired MLD test results were compared with evaluate repeatability. The MLD test showed excellent repeatability. The choice of definition of IMI from milk culture had minor effects on estimates of MLD and CMT test characteristics. For EL samples, when interpreting MLD and CMT results at the quarter level, and regardless of the referent test used, both tests had low sensitivity (MLD = 11.7-39.1%; CMT = 0-52.2%) but good to very good specificity (MLD = 82.1-95.2%; CMT = 68.1-100%), depending on the cut-point used. Sensitivity improved slightly if diagnosis was interpreted at the cow level (MLD = 25.6-56.4%; CMT = 0-72.2%), though specificity generally declined (MLD = 61.8-100%; CMT = 25.0-100%) depending on the cut-point used. For LL samples, when interpreted at the quarter level, both tests had variable sensitivity (MLD = 46.6-84.8%; CMT = 9.6-72.7%) and variable specificity (MLD = 59.2-79.8%; CMT = 52.5-97.3%), depending on the cut-point used. Test sensitivity improved if interpreted at the cow level (MLD = 59.6-86.4%; CMT = 19.1-86.4%), though specificity declined (MLD = 32.4-56.8%; CMT = 14.3-92.3%). Producers considering adopting either test for LL or EL screening programs will need to carefully consider the goals and priorities of the program (e.g., whether to prioritize test sensitivity or specificity) when deciding on the level of interpretation (quarter or cow) and when selecting the optimal cut-point for interpreting test results. Additional validation studies and large randomized field studies will be needed to evaluate the effect of adopting either test in selective dry cow therapy or fresh cow screening programs on udder health, antibiotic use, and economics.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".