Diagnosing intramammary infections: Comparison of multiple versus single quarter milk samples for the identification of intramammary infections in lactating dairy cows
Bibliographic record
Abstract
The objective was to examine the potential benefits of using different combinations of multiple quarter milk samples compared with a single sample for diagnosing intramammary infections (IMI) in dairy cattle. Data used in the analyses were derived from 7,076 samples from 667 quarters in 176 cows in 8 herds in 4 locations (Minnesota/Wisconsin, n=4; Prince Edward Island, n=2; Ontario, n=1; New York, n=1). Duplicate quarter milk samples were collected at morning milking for 5 consecutive days. Cows were evenly distributed between early postparturient and mid- to late-lactation cows. All samples were frozen for shipping and storage, thawed once, and cultured in university laboratories using standardized procedures consistent with National Mastitis Council guidelines. The presence of specific pathogens was confirmed and identified using the API identification system (bioMerieux, Marcy l'Etoile, France) in each laboratory. A previously developed gold standard was applied to the first sample from d 1, 3, and 5 to classify infected quarters. The data were analyzed separately for coagulase-negative staphylococci (CNS) and Streptococcus spp. Various combinations of test results from d 2 and 4 were used in the test evaluation. These consisted of single samples (n=4), 2 sets of duplicate samples (2 samples collected on the same day), 2 sets of consecutive samples (2 samples collected 2 d apart), and 2 sets of triplicate samples (2 samples on the same day and a third sample 2 d apart). Series interpretation of duplicate or consecutive samples (i.e., positive=same pathogen isolated from both samples) resulted in the highest specificity (Sp; CNS Sp=92.1-98.1%; Streptococcus spp. Sp=98.7-99.6%), but lowest sensitivity (Se; CNS Se=41.9-53.3%; Streptococcus spp. Se=7.7-22.2%). Parallel interpretation of duplicate or consecutive samples (i.e., positive=pathogen isolated from either) resulted in the highest Se (CNS Se=70.8-80.6%; Streptococcus spp. Se=31.6-48.1%), but lowest Sp (CNS Sp=72.0-77.3%; Streptococcus spp. Sp=89.5-93.3%). The difference in estimates between single and duplicate samples was larger than between single and consecutive samples. Overall, triplicate samples provided the best combination of Se and Sp, but compared with a single sample, provided only a modest gain in Sp and little or no gain in Se.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".