Bayesian diagnostic accuracy estimation of milk enzyme-linked immunosorbent assay, blood polymerase chain reaction, and peripheral blood lymphocyte count tests to determine bovine leukosis virus status in dairy cows
Bibliographic record
Abstract
We assessed the diagnostic accuracy of an adapted antibody ELISA (ELISA-Ab) test, originally designed for bulk milk samples but applied on individual DHI-collected milk samples, to identify the bovine leukosis virus infection status of individual cows. Blood real-time PCR (qPCR) and blood lymphocyte count (LC) tests were used for comparison. For the milk ELISA-Ab, secondary objectives included identifying a fit-for-purpose threshold for result interpretation and evaluating whether the test's specificity could be influenced by the sampling technique (i.e., DHI-collected milk samples). Additionally, we evaluated whether the accuracy of each test varied with cow age, categorizing cows as young (2-4 yr old) or older (>4 yr old). In 2023, 8 dairy herds in Québec, Canada, were selected based on their historical within-herd leukosis prevalence, which was estimated to range from 10% to 75%. From all milking cows within these herds (n = 637), milk samples were collected during regular DHI, and blood samples were collected by the research team within one week of the DHI sampling. The indirect IDEXX Leukosis Milk Screening ELISA test was adapted to accommodate individual cow milk samples (as opposed to bulk tank milk samples), whereas an in-house qPCR assay targeting gag-pro-pol gene regions and LC determination were applied to blood samples. Bayesian latent class models were used to estimate the diagnostic accuracy of the tests. An optical density threshold of ≥0.5 for the ELISA-Ab provided an optimal control of the misclassification cost across various leukosis prevalence and, to a lesser extent, false negative to false positive cost ratio scenarios. With this threshold, the sensitivity and specificity estimates (95% Bayesian credible interval [BCI]) were 92% (BCI: 88%, 95%) and 99% (BCI: 96%, 100%), respectively. Sensitivity was higher in cows >4 yr old (99%, BCI: 96%, 100%) compared with cows 2 to 4 yr old (88%, BCI: 80%, 94%). We observed lower ELISA-Ab specificity in cows milked immediately after a positive cow (median: 82%, BCI: 72%, 97%) compared with those milked after a negative cow (median: 91%, BCI: 85%, 99%), suggesting a milk carryover effect due to the sampling technique. This carryover effect had a more pronounced impact on the false positive rate in herds with 30% to 50% leukosis prevalence, with the largest differences observed at the 30% prevalence scenario. However, the overall influence of the carryover effect remained limited. The qPCR test showed a sensitivity of 81% (BCI: 75%, 86%) and a specificity of 100% (98%, 100), whereas the LC test had a sensitivity of 55% (49%, 61%) and a specificity of 96% (93%, 98%). Both the qPCR and LC test accuracy parameters remained similar across age groups. In conclusion, the adapted ELISA-Ab test appears suitable for individual cow testing using DHI-collected milk samples, with higher sensitivity in cows >4 yr old. Its integration into existing milk recording programs provides a practical opportunity for herd-level leukosis monitoring.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".