1–10-100: Unifying goals to mobilize global action on antimicrobial resistance
Bibliographic record
Abstract
The Bellagio Group for Accelerating AMR Action met in April 2024 to develop the ambitious but achievable 1-10-100 unifying goals to galvanize global policy change and investments for antimicrobial resistance mitigation: 1 Health; 10 million lives saved; and 100% sustainable access to effective antimicrobials. High profile political goals such as the Paris Agreement's objective to keep global warming well below 2° Celsius compared to pre-industrial levels, UNAIDS' 90-90-90 goal, and the Sustainable Development Goals challenge global norms, direct attention towards relevant activities, and serve an energizing function to motivate action over an extended period of time. The 1-10-100 unifying goals propose to unite the world through a One Health approach to safeguard human health, animal welfare, agrifood systems, and the environment from the emergence and spread of drug-resistant microbes and infections; save over 10 million lives by 2040 through concerted efforts to prevent and appropriately treat infections while preserving the vital systems and services that depend on sustained antimicrobial effectiveness; and commit to ensuring that antimicrobials are available and affordable for all, used prudently, and secured for the future through innovation. Compared to existing technical targets, these unifying goals offer advantages of focusing on prevention, encouraging multisectoral action and collaboration, promoting health equity, recognizing the need for innovation, and integrating with Sustainable Development Goals. By committing to 1 Health, 10 million lives saved, and 100% sustainable access to effective antimicrobials, we can protect lives and livelihoods today and safeguard options for tomorrow.
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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 | 0.035 | 0.027 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.021 |
| Research integrity | 0.027 | 0.018 |
| Insufficient payload (model declined to judge) | 0.016 | 0.014 |
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".