Bibliographic record
Abstract
Antimicrobial resistance affects the delivery of safe and effective healthcare. Antimicrobial resistance has attracted strong political focus, with the 2024 United Nations General Assembly high level meeting providing a clear commitment to reducing mortality and improving antibiotic use. This review summarises recent political action, policy prioritisation, and identification of future threats. It considers infections that are caused by drug resistant pathogens and reviews available and new antibiotics that may meet unmet medical needs. Despite increasing political engagement, the global antimicrobial resistance landscape remains imbalanced. In high income hospital settings, diagnostics, antimicrobial stewardship, and infection prevention and control are improving and may be further enabled by artificial intelligence and information systems. The development and use of new antibiotics is a major focus. By contrast, in low and middle income countries, access to most of these advances is limited. In all settings, empirical prescribing of essential antibiotics remains the cornerstone of treatment and conserving their efficacy is critical to effective healthcare. Targeted prevention and optimal treatment strategies are needed to mitigate antimicrobial resistance across all settings.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".