Developing a framework for a western Canadian companion animal surveillance initiative: Case definitions and the role of the veterinarian.
Bibliographic record
Abstract
Objective: Surveillance data on companion animals in western Canada are extremely limited. Previous research by the principal investigators established a list of potential canine pathogens of relevance to public health for inclusion in the Western Canadian Companion Animal Surveillance Initiative (CASI). Our objective was to assess veterinary interest in contributing to companion animal surveillance, and to gather baseline data on specific canine pathogens of interest to create surveillance-specific case definitions. Procedure: An invitation to participate in an online survey was disseminated to all clinical veterinarians across the provinces of Alberta, Saskatchewan, and Manitoba. Results: There was a moderate level of interest (median: 7.5/10) from veterinarians to participate in the surveillance of companion animals. The majority (85%, 51/60) of veterinarians participating in the survey recorded diagnosing at least 1 of the pathogens of interest over a 5-year interval. Based on survey responses, several surveillance case definitions were formulated for pathogen groups of interest, most of which require laboratory testing for confirmation. Conclusion and clinical relevance: This study identified the willingness, practicality, and importance of veterinarians or veterinary clinics participating in companion animal surveillance.
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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 | 0.152 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.015 | 0.007 |
| Science and technology studies | 0.011 | 0.019 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.013 | 0.017 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".