Analytical specificity and sensitivity of a real-time polymerase chain reaction assay for identification of bovine mastitis pathogens
Bibliographic record
Abstract
Intramammary infection (IMI), also known as mastitis, is the most frequently occurring and economically the most important infectious disease in dairy cattle. This study provides a validation of the analytical specificity and sensitivity of a real-time PCR-based assay that identifies 11 major pathogen species or species groups responsible for IMI, and a gene coding for staphylococcal beta-lactamase production (penicillin resistance). Altogether, 643 culture isolates originating from clinical bovine mastitis, human, and companion animal samples were analyzed using the assay. The isolates represented 83 different species, groups, or families, and originated from 6 countries in Europe and North America. The analytical specificity and sensitivity of the assay was 100% in bacterial and beta-lactamase identification across all isolates originating from bovine mastitis (n = 454). When considering the entire culture collection (including also the isolates originating from human and companion animal samples), 4 Streptococcus pyogenes, 1 Streptococcus salivarius, and 1 Streptococcus sanguis strain of human origin were identified as Streptococcus uberis, and 3 Shigella spp. strains were identified as Escherichia coli, decreasing specificity to 99% in Strep. uberis and to 99.5% in E. coli. These false-positive results were confirmed by sequencing of the 16S rRNA gene. Specificity and sensitivity remained at 100% for all other bacterial targets across the entire culture collection. In conclusion, the real-time PCR assay shows excellent analytical accuracy and holds much promise for use in routine bovine IMI testing programs. This study provides the basis for evaluating the assay's diagnostic performance against the conventional bacterial culture method in clinical field trials using mastitis milk samples.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".