When Antibiotics Fail: The Expert Panel on the Potential Socio-Economic Impacts of Antimicrobial Resistance in Canada
Bibliographic record
Abstract
Antimicrobials are life savers in Canada, enabling modern healthcare and playing a central role in agriculture. They have reduced the economic, medical, and social burden of infectious diseases and are part of many routine medical interventions, such as caesarean sections, joint replacements, and tonsillectomies. As use of antimicrobials has increased, bacteria evolved to become resistant, resulting in drugs that are no longer effective at treating infections. Antimicrobial resistance (AMR) is increasing worldwide, and with widespread trade and travel, resistance can spread quickly, posing a serious threat to all countries. For Canada, the implications of AMR are stark. When Antibiotics Fail examines the current impacts of AMR on our healthcare system, projects the future impact on Canada’s GDP, and looks at how widespread resistance will influence the day-to-day lives of Canadians. The report examines these issues through a One Health lens, recognizing the interconnected nature of AMR, from healthcare settings to the environment to the agriculture sector. It is the most comprehensive report to date on the economic impact of AMR in Canada.
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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 | 0.017 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.013 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".