A scoping review of the testing of bulk tank milk to detect nonbacterial pathogens or herd exposure to nonbacterial pathogens in dairy cattle
Bibliographic record
Abstract
In this scoping review, we characterized the literature reporting on the testing of bulk milk samples to detect microorganisms other than bacteria that can cause diseases in dairy cattle, including viruses, helminths, algae, and protozoa. A search strategy was completed by screening databases, conference proceedings, animal health agency websites, disease surveillance program websites, and handbooks of cattle-related diagnostic tests for potentially relevant articles. Two reviewers independently screened articles in English, Portuguese, or Spanish; original studies reporting on the testing of farm-level, unprocessed bulk milk samples for presence of pathogens or specific antibodies against agents other than bacteria that can cause diseases in cows were retained. From all studies, we used spreadsheets to extract relevant information, including pathogen screened, test used, and country of origin of bulk milk samples. Additionally, for studies reporting sufficient data to estimate test characteristics, we extracted detailed information about herd eligibility, testing protocol, and herd-level infection definition. A total of 8,829 records were identified, from which 1,592 were retained and assessed for eligibility, and 306 were included. Bovine viral diarrhea virus, Fasciola hepatica, Ostertagia ostertagi, and bovine herpesvirus 1 were the most frequently screened agents, reported from 107, 45, 45, and 33 studies, respectively. Sensitivity of bulk milk ELISA to detect herds with animals infected by bovine herpesvirus 1 ranged from 2 to 100%, and was affected mostly by antigen selection, cut-off adopted, herd vaccination status, and seroprevalence of lactating cows. Bulk milk ELISA had very high specificity to detect herds free of bovine leukemia virus, and varying sensitivity to detect herds with infected animals, which depended on the within-herd seroprevalence of lactating cattle. As for bovine viral diarrhea virus, in general, the sensitivity of bulk milk ELISA was moderate to high (>80%) when infection status was defined based on presence of persistently infected cattle or a high proportion of seropositive lactating cattle. Nevertheless, bulk milk ELISA was not able to distinguish infected and noninfected herds based on presence of seropositive unvaccinated weanlings. The PCR or quantitative PCR protocols employed had very low sensitivities (<40%) and very high specificities (>95%) to classify bovine viral diarrhea virus infection status of dairy herds. Sensitivity and specificity of bulk milk ELISA to classify herds with regards to presence of F. hepatica- or O. ostertagi-parasitized cattle were generally high and driven mostly by the definition of herd infection status. Conversely, bulk milk ELISA demonstrated varying characteristics to detect herds with or without Dictyocaulus viviparus-parasitized cattle, depending primarily on the antigen selected and presence of cattle with clinical signs of lungworm infection.
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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 | 0.015 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.024 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".