Comparison of fecal pooling strategies for detection of Mycobacterium avium ssp. paratuberculosis in cattle
Bibliographic record
Abstract
In herds with typical moderate to low within-herd prevalence, testing for Mycobacterium avium ssp. paratuberculosis (MAP), the infectious agent of Johne's disease, will be more cost-effective if individual fecal samples are cultured in composite pools. However, sensitivity to classify a pool containing 1 or more positive individual samples as positive may depend on pool size and number of individual positive samples within a pool. Fecal samples collected from 994 dairy cows sampled at slaughter were cultured to detect MAP. Culturing was done both individually and as composite pooled samples using the TREK ESP Culture System II broth medium (Thermo Fisher Scientific, Trek Diagnostic Systems Inc., Cleveland, OH). Composite samples consisted of pools containing feces from 3, 5, 8, 10, or 15 cows. The number of individual fecal culture-positive cows within each pool ranged from 0 to 4. Culture of individual fecal samples detected MAP in 36 (3.6%) of the 994 cows. Individual samples that were detected within the first 50 d by TREK ESP Culture System II were more likely to lead to a positive pool result. In total, 840 pooled fecal samples were examined for presence of MAP, and of those, 272 pools actually contained feces from fecal culture-positive cows. The crude sensitivity (proportion of pools that contained at least 1 fecal-positive cow that tested positive) for pools of 3, 5, 8, 10, and 15 was 47, 67, 44, 59, and 39%, respectively. Across pools, an increase of the number of fecal culture-positive samples from 1 to 2 enhanced overall crude sensitivity from 44 to 71%. However, sensitivity did not further increase for pools with 3 or 4 fecal culture-positive samples (63 and 60%, respectively). Additionally, a simulation analysis assessing probability of pooled fecal samples being positive in herds of 50 and 100 cows was conducted. The simulation assumed that 1, 2, or 5 cows per herd were MAP fecal culture-positive and that pools of 5 and 10 were used. This low-prevalence herd simulation indicated that weighted mean herd probabilities of detecting a positive herd ranged between 52 and 99.3%, with the lowest probability for pools of 10 with 1 positive cow in the herd and the highest probability for pools of 5 with 5 positive cows in the herd. However, overall, pools of 5 and 10 had similar diagnostic capabilities, enabling cost savings by utilizing pools of 10.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".