Assessing the Value of Antibiotics on Farms: Modeling the Impact of Antibiotics and Vaccines for Managing Lawsonia intracellularis in Hog Production
Bibliographic record
Abstract
Increasing awareness of antibiotic resistance has correspondingly increased efforts to identify and reduce the causal behaviours that led to this severe public health threat. The consequences of these efforts are regulatory and market pressures limiting antibiotic use by livestock farmers which may lead to significant financial and welfare challenges on the farm, even if antibiotics can be substituted by vaccines. The purpose of this study is to measure the relative cost-effectiveness of antibiotics versus vaccines for controlling L. intracellularis on a Canadian farrow-to-finish pig farm. This is done by modelling the production and economic impact of different antibiotics and vaccines available for managing this disease, listed in the Canadian Compendium of Veterinary Products. The economic impacts (in Canadian dollars) of the disease are estimated and the net benefits of alternative prevention and treatment options are compared to determine the relative cost-effectiveness of each strategy. Of the twelve options analyzed, four were preventative (antibiotic and vaccine) and eight were antibiotic treatments. Prophylactic chlortetracycline (an antibiotic) is the most cost-effective option for managing L. intracellularis, while Porcilis Ileitis (a vaccine) is the least cost-effective strategy. This result remains robust considering sensitivity analysis of the production parameters, which indicates that preventative antibiotics are more cost-effective than vaccines. This implies that banning preventative antibiotic treatments harms the bottom line of farmers under current market conditions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".