Progress in Paratuberculosis Control Programmes for Dairy Herds
Bibliographic record
Abstract
While paratuberculosis control has been studied for over a century, knowledge gaps still exist regarding the uptake and efficacy of control programmes. This narrative review aims to summarise studies on control programmes presented at the IDF ParaTB Fora in 2021 and 2022 and the International Colloquium on Paratuberculosis in 2022. Studies were grouped by topic as follows: successful control, field studies, education and extension, voluntary and compulsory control programmes, and surveillance. Various Map control programmes resulted in a decreasing animal and herd level Map prevalence. Long-term stakeholder commitment, stable funding, involvement of herd veterinarians and incentives for farmers to participate were shown to be pivotal for long-term success. Control measures focused on vertical and calf-to-calf transmission may improve Map control in infected herds. Easy-to-capture visualisation of surveillance test results to inform participants on the progress of Map control in their herds was developed. The probability of freedom from disease and estimated within-herd prevalence were identified as good candidates for categorisation of herds to support low-risk trade of cattle. Results of the surveillance schemes may inform genetic selection for resistance to Map infection. In conclusion, successful paratuberculosis control is feasible at both the herd and country level provided that crucial prerequisites are met.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".