Evaluation of fecal culture and fecal RT-PCR to detect Mycobacterium avium ssp. paratuberculosis fecal shedding in dairy goats and dairy sheep using latent class Bayesian modeling
Bibliographic record
Abstract
BACKGROUND: The study's objective was to evaluate the ability of fecal culture (FCUL) and fecal PCR (FPCR) to identify dairy goat and dairy sheep shedding Mycobacterium avium ssp. paratuberculosis. A cross-sectional study of the small ruminant populations was performed in Ontario, Canada between October 2010 and August 2011. Twenty-nine dairy goat herds and 21 dairy sheep flocks were visited, and 20 lactating females > two years of age were randomly selected from each farm resulting in 580 goats and 397 sheep participating in the study. Feces were collected per rectum and cultured using the BD BACTEC™ MGIT™ 960 system using a standard (49 days) and an extended (240 days) incubation time, and underwent RT-PCR based on the hsp-X gene (Tetracore®). Statistical analysis was performed using a 2-test latent class Bayesian hierarchical model for each species fitted in WinBUGS. RESULTS: Extending the fecal culture incubation time statistically improved FCUL sensitivity from 23.1 % (95 % PI: 15.9-34.1) to 42.7 % (95 % PI: 33.0-54.5) in dairy goats and from 5.8 % (95 % PI: 2.3-12.4) to 19.0 % (95 % PI: 11.9-28.9) in dairy sheep. FPCR demonstrated statistically higher sensitivity than FCUL (49 day incubation) with a sensitivity of 31.9 % (95 % PI: 22.4-43.1) in goats and 42.6 % (95 % PI: 28.8-63.3) in sheep. CONCLUSIONS: Fecal culture demonstrates such low sensitivity at the standard incubation time it cannot be recommended as a screening test to detect shedding of MAP in either goats or sheep. Extending the incubation time resulted in improved sensitivity; however, it is still disappointingly low for screening purposes. Fecal PCR should be the screening test of choice in both species; however, it is important to recognize that control programs should not be based on testing alone when they demonstrate such low sensitivity.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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 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".