Should we stop vaccinating against foot-and-mouth disease in Uruguay?
Bibliographic record
Abstract
Veterinarians’ opinions are key to successfully implementing disease control programs. Foot-and-mouth disease (FMD) has a significant economic impact due to animal production losses and trade restrictions. In 2010, PANAFTOSA defined a roadmap to FMD eradication in South America. Although Uruguay has implemented this plan by using mandatory vaccination since the last outbreak in 2001, vaccination restricts access to premium export markets. The objective of this study was to determine the perception of veterinarians involved in large animal disease control programs (accredited veterinarians) on a future FMD control stage without vaccination in Uruguay. Two hundred and fifty-six accredited veterinarians were surveyed between August and September 2018. Two strata were defined as follows: Stratum 1 (pre-FMD outbreak), veterinarians who enrolled in the University of the Republic in Uruguay before or in 2001 (N=708), and Stratum 2 (post-FMD outbreak) veterinarians who enrolled after 2001 (N=426). Data related to demographics, vaccination perceptions, and FMD experience were collected through online and phone interviews. Logistic regressions were used to determine the association between demographic variables, FMD-related risk perceptions, and the willingness to stop FMD vaccination. 41.7% (± 4.2) and 29.4% (± 4.2) of veterinarians, for strata 1 or 2, respectively, were willing to stop vaccination. Veterinarians’ geographical region of work influenced this perception. The northeast area being more likely to support stopping vaccination (46.3 ± 5.8%) when compared to the South-center (39.2 ± 4.9%) and West (25.3 ± 5.0%). Accredited veterinarians are still hesitant to stop vaccination, presenting problems when implementing a non-vaccination stage.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".