Most likely causes of infection and risk factors for tuberculosis in Spanish cattle herds
Bibliographic record
Abstract
INTRODUCTION: This study aims to assess the most likely causes of Bovine tuberculosis (bTB) breakdowns in Spanish cattle herds and to identify the main risk factors at farm-level. METHODS: Causes of bTB breakdowns were assessed through a qualitative risk-assessment based on decision-trees by analysing surveillance data from 3819 bTB breakdowns detected during 2014-2016. Results were compared to veterinary officers' (VO) opinions. Risk factors were identified through a case-control study with data from 196 bTB cases and 160 controls collected during 2014-2018. RESULTS: The decision tree analysis identified residual infections and interactions with wildlife as the most frequent causes of breakdowns (36% each), followed by purchasing infected cattle (14%). These results were not supported by VOs' opinions. According to the regression models, the risk of bTB increased by sharing pastures (odds ratios [OR] = 2.7; 95% confidence interval [CI] = 1.6-4.4) and by increasing inwards cattle movements. The presence of wildlife reservoirs represented a significant risk for extensively-managed farms if other cattle farms are situated within a one-kilometre radius (OR = 2.3; 95% CI = 1.1-5.1). DISCUSSION: To prevent bTB breakdowns, efforts should be devoted to decrease the likelihood of residual infections and improve farm biosecurity. The adoption of biosecurity measures might be influenced by farmers' perceptions, which should be carefully evaluated to ensure the effectiveness of such strategies.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".