System-wide approaches to antimicrobial therapy and \nantimicrobial resistance in the UK: the AMR-X framework
Bibliographic record
Abstract
Antimicrobial resistance (AMR) threatens human, animal, and environmental health. Acknowledging the urgency of \naddressing AMR, an opportunity exists to extend AMR action-focused research beyond the confines of an isolated \nbiomedical paradigm. An AMR learning system, AMR-X, envisions a national network of health systems creating and \napplying optimal use of antimicrobials on the basis of their data collected from the delivery of routine clinical care. \nAMR-X integrates traditional AMR discovery, experimental research, and applied research with continuous analysis of \npathogens, antimicrobial uses, and clinical outcomes that are routinely disseminated to practitioners, policy makers, \npatients, and the public to drive changes in practice and outcomes. AMR-X uses connected data-to-action systems to \nunderpin an evaluation framework embedded in routine care, continuously driving implementation of improvements \nin patient and population health, targeting investment, and incentivising innovation. All stakeholders co-create \nAMR-X, protecting the public from AMR by adapting to continuously evolving AMR threats and generating the \ninformation needed for precision patient and population care.
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 | 0.032 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.004 | 0.029 |
| Scholarly communication | 0.017 | 0.011 |
| Open science | 0.004 | 0.017 |
| Research integrity | 0.011 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".