Risk Factors Associated with Transmission of <i>Mycobacterium avium</i> subsp. <i>paratuberculosis</i> to Calves within Dairy Herd: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Paratuberculosis has a worldwide distribution and many countries have implemented control programs to prevent transmission among and within herds. For these programs to be efficient, knowledge of the risk factors involved in transmission is essential. OBJECTIVES: Systematically review the scientific literature concerning risk factors associated with Mycobacterium avium subsp. paratuberculosis (MAP) transmission to dairy calves. STUDY DESIGN: Systematic review. METHODS: An electronic search was done in PubMed and CAB to retrieve references relevant to answer at least 1 of the 5 questions concerning neonatal environment, colostrum, milk, housing of calves, and contact of calves with adult cow feces as risk factors in MAP transmission. A 1st screening was done using titles only, then abstracts, and finally full-length articles were reviewed for relevance. From the articles selected, risk factors and presence of a significant association between these risk factors and MAP transmission were recorded. RESULTS: Twenty-three articles from 11 different countries and published in 12 different journals were reviewed. The most common study design was cross-sectional (n = 16). The case definition and diagnostic tests used were very variable among studies, but serum ELISA was used in most studies (n = 14). The study unit was the herd in 18 studies. CONCLUSIONS AND CLINICAL IMPORTANCE: The contact of calves with adult cow feces is the most important risk factor in MAP transmission. The 5 categories of risk factors are linked to one another.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".