The innovative and purpose-built veterinary antimicrobials sales reporting system
Bibliographic record
Abstract
Background: Antimicrobial resistance (AMR) is one of the major public health threats of our time. Human activities across the One Health spectrum, such as the misuse and overuse of antimicrobials, can accelerate the resistance threat. A variety of antimicrobials used in veterinary medicine are also important in human medicine. As part of Canada's commitment to address AMR and antimicrobial use (AMU), and to align with international best practices aimed to minimize the impacts of AMR and preserve the effectiveness of existing antimicrobials, regulatory controls and enhanced surveillance initiatives have been implemented in veterinary medicine and animal health to improve intelligence on the quantities of antimicrobials available for use in animals. These efforts include the implementation of the national Veterinary Antimicrobial Sales Reporting (VASR) system in Canada, in 2018. The focus of this article is to describe the VASR data collection system and platform. Methods: A custom-built data collection and analytical system was developed to enhance understanding of the volume of antimicrobials available for use in animals and contribute to the broader surveillance of trends in AMU and AMR in an effort to support stewardship. Partners from Health Canada's Veterinary Drugs Directorate, the Public Health Agency of Canada's Centre for Food-borne, Environmental and Zoonotic Infectious Diseases, and the Canadian Network for Public Health Intelligence worked together to envision, develop, and implement the national-scale, purpose-built technological intervention, the VASR system. Results: The VASR surveillance system provides a robust data collection and analytical informatics platform to improve intelligence on antimicrobial sales available for veterinary use in Canada. Conclusion: An innovative, purpose-built national antimicrobial sales reporting system was developed. This web-based platform is effective for data submission by the participants and facilitates analysis to provide a comprehensive picture of medically important antimicrobials available for use in animals in Canada, thereby supporting AMR and AMU surveillance and stewardship.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".