Effect of season and lactation stage on the diagnostic sensitivity of direct real-time PCR assay for detection of fecal shedding of Mycobacterium avium subsp paratuberculosis
Bibliographic record
Abstract
Mycobacterium avium subsp paratuberculosis (MAP) is the causative organism of Johne's disease. Dairy Farmers of Canada lists this production-limiting disease as one of the top two animal health priorities for the Canadian dairy industry. Infection of newborn calves from ingestion of MAP-infected feces, colostrum, or milk is considered the main route of transmission and a major concern in a herd. Current diagnostics rely on identification of the bacterium in feces via culture and molecular tests or identification of MAP antibodies in milk or serum via enzyme-linked immunosorbent assays (ELISA). However, these tests are inadequate to meet industry needs for herd biosecurity and environmental-transmission control of MAP, especially when MAP-infected cows are in the subclinical stages of the disease. Milk ELISAs have poor predictive values due to imperfect sensitivity and specificity, especially when used in herds with a low prevalence of MAP-infected cows. Although culture of fecal samples for MAP is currently the gold standard diagnostic test for identification of MAP-infected cows, the long incubation times, costs, and intermittent shedding of MAP in feces hinder its use as an efficient screening tool. The goal of this study was to assess how shedding patterns of MAP in feces vary with lactation stage and season, as determined via direct real-time polymerase chain reaction (PCR) assay.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".