Paratuberculosis in the Small Ruminant Dairy Industries of Ontario: Prevalence, Risk Factors, and Test Evaluations
Bibliographic record
Abstract
This thesis was to determine the prevalence and distribution of paratuberculosis in the Ontario dairy sheep and dairy goat industries, identify potential risk factors for herds which tested positive, evaluate the accuracy of seven commercially available individual and two bulk tank diagnostic tests in these two populations, and determine the circulating strains of Mycobacterium avium ssp. paratuberculosis in faecal isolates obtained. A cross-sectional study was conducted between October 2010 and August 2011 in 29 goat herds and 21 sheep flocks located in Ontario. On each farm, 20 lactating animals over the age of two years were randomly selected and faeces, blood, and milk were sampled from each animal, and a bulk milk sample from each herd. A questionnaire inquiring about herd management and biosecurity behaviours was also completed. The seven individual animal tests evaluated were: faecal culture using the BACTEC® MGIT™ 960 liquid culture system, direct faecal PCR (Tetracore®, Rockville, MD) based on the hspX gene, the Prionics® ELISA on serum and milk, the IDEXX® ELISA on serum and milk, and the agar gel immunodiffusion (AGID) test on serum. The test evaluations used both frequentist (faecal culture - reference test) and Latent Class Analysis/Bayesian (LCA/BM) methods (no reference test). In goat herds, faecal culture demonstrated the highest sensitivity (Se), 81.1% (LCA/BM). In sheep, while faecal culture demonstrated the highest Se, 49.5%, there was a small probability it was higher than faecal PCR Se at 42.4%. The bulk tank tests evaluated were the 'Hyper-ELISA' test and real-time PCR test based on IS900 (AntelBio®). While PCR did not demonstrate sufficiently high Se to be used as a herd-level test, the Hyper-ELISA performed well as a herd-level test identifying farms with high prevalence when the cut-off was reduced to 0.05. Overall herd-level apparent prevalence was 79.3% in goat herds and 57.1% in sheep flocks when faecal culture was the reference standard and true herd-level prevalence (LCA/BM) was 83.0% and 66.8% in each population respectively. This high prevalence reveals a need for the implementation of a small ruminant paratuberculosis control program in Ontario, Canada based on testing, improving youngstock management, and strengthening biosecurity practices.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".