Strengthening Policy Relevance of Wastewater-Based Surveillance for Antimicrobial Resistance
Bibliographic record
Abstract
A ntimicrobial resistance (AMR) is among the top 10 public health threats, with nearly 5 million deaths in 2019 linked to AMR-related bacterial infections. 1 A One Health approach is needed to combat AMR.Healthcare-based surveillance (HBS) of AMR provides incomplete information about the scope of the AMR threat.HBS screens only patients seeking medical attention, lacking community-level representativeness, and suffers from underreporting. 2 Consequently, researchers are turning to wastewater-based surveillance (WBS) to complement HBS. 3 WBS can provide information about AMR circulating within communities and hospitals, offering a comprehensive understanding of AMR prevalence.However, the surveillance targets and data obtained from WBS are distinct from those derived from HBS, creating uncertainty regarding their utility to the public health sector and ability to yield policy relevant information.In May 2024, participants in a workshop during the 7 th Environmental Dimension of Antimicrobial Resistance (EDAR7) conference (Montreál, Canada) sought to answer
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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 | 0.089 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.015 | 0.012 |
| Open science | 0.005 | 0.013 |
| Research integrity | 0.026 | 0.013 |
| Insufficient payload (model declined to judge) | 0.013 | 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".