Describing frameworks that may be adapted to Canadian national surveillance of antimicrobial usage amongst dogs & cats: Scoping review protocol
Bibliographic record
Abstract
This scoping review will assess the literature to identify reported systems or frameworks conducting surveillance/monitoring in companion animals (dogs and/or cats) of veterinary antimicrobial and/or other therapeutic drug use, including prescription or sales data, that could be adapted to Canadian national surveillance of antimicrobial use (AMU) in these species. Addressing gaps in AMU surveillance from companion animals is essential in working towards a comprehensive One Health approach to addressing the overall issue of antimicrobial resistance (AMR) in Canada, as AMU is a known driver of AMR.
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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 | 0.193 | 0.244 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.007 | 0.016 |
| Bibliometrics | 0.037 | 0.037 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.060 | 0.011 |
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".