Carryover of bovine leukemia virus antibodies in samples from shared milk meters
Bibliographic record
Abstract
Screening for infectious diseases of cattle using milk from the dairy herd improvement (DHI) sampling process is very convenient. However, when samples from shared milk meters are used, carryover of antibodies or other diagnostic targets can complicate the interpretation of the diagnostic test results for diseases, including bovine leukosis. The objectives of this study were (1) to assess the potential for carryover of antibodies against bovine leukemia virus (BLV) in milk samples obtained from shared meters, and (2) to determine if adjustment of the diagnostic test cut-off value would improve the test characteristics for meter-collected milk ELISA results. Eight dairy farms were randomly selected from herds with a wide range of BLV prevalence levels in Prince Edward Island, Canada. Within each chosen farm, 2 to 4milk meters were randomly selected. During the routine procedures of DHI sampling, 2 simultaneous milk samples, 1 hand-collected at the beginning of milking (after udder preparation) and the other from the corresponding milk meter, were taken from all lactating cows (n=236) that were milked at the selected meters (n=26). The sequence of cows using each meter was recorded. All samples were tested for BLV antibodies using a commercial indirect ELISA. Antibody carryover potential was assessed in meter-collected samples which were preceded by other cows using the same meters. Applying the hand-collected sample results as our reference standard, a new cut-off was defined for meter-collected samples to optimize the test characteristics. At the standard cut-off value of the diagnostic test, 110 (46.6%) of the hand-collected and 136 (57.6%) of the meter-collected samples were positive. For low-titer cows (e.g., true negatives), the likelihood of antibody carryover significantly increased as the titer of preceding cows increased, whereas this change was not substantial for high-titer cows. The odds of obtaining false diagnoses in meter-positive samples became larger with increase in the titer of preceding cows. A suspicious category for meter ELISA results was defined, and a retest was recommended for the cows falling into this category. This strategy effectively assisted in reducing the number of consequent false-positive results. When DHI-collected samples are used, carryover can affect the interpretation of dichotomous test results and may require adjustment of assay cut-off values.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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 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".