Overview of Canada's Antimicrobial Resistance Network (AMRNet): A data-driven One Health approach to antimicrobial resistance surveillance
Bibliographic record
Abstract
The Antimicrobial Resistance Network (AMRNet) is a laboratory-based antimicrobial resistance (AMR) surveillance system under development at the Public Health Agency of Canada's (PHAC's) National Microbiology Laboratory. The AMRNet surveillance system captures information on antimicrobial susceptibility testing from clinical and veterinary laboratories including both public and private facilities. In the future, the AMRNet system will also capture relevant data from existing PHAC surveillance systems for AMR including the Canadian Integrated Program for Antimicrobial Resistance Surveillance, the Canadian Nosocomial Infection Surveillance Program and the Enhanced Surveillance of Antimicrobial-Resistant Gonorrhea program, and contribute to the Canadian Antimicrobial Resistance Surveillance System. AMRNet's integrated "One Health" approach will allow health professionals and researchers to take a multi-dimensional perspective of AMR in both human and animal health in Canada and will make Canada a leader in AMR surveillance. AMRNet is a collaboration between PHAC, provincial and territorial public health organizations as well as clinical and veterinary laboratories across the country. As part of a phased rollout, AMRNet is now collecting human clinical data from three provinces, from both inpatients and outpatients. Ultimately, AMRNet aims to capture all antimicrobial susceptibility testing results from all bacterial and fungal pathogens across Canada. This article describes the AMRNet surveillance system, including program objectives, system structure and the data collected. The integration of human and animal data in AMRNet will inform One Health responses to AMR issues. The capacity to collect and to disseminate data to stakeholders in real time is a critical step to addressing emerging AMR issues in Canada.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.015 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".