WHO's essential medicines and AWaRe: recommendations on first- and second-choice antibiotics for empiric treatment of clinical infections
Bibliographic record
Abstract
Abstract The WHO Model List of Essential Medicines (EML) prioritizes medicines that have significant global public health value. The EML can also deliver important messages on appropriate medicine use. Since 2017, in response to the growing challenge of antimicrobial resistance, antibiotics on the EML were reviewed and categorized into three groups: Access, Watch and Reserve, leading to a new categorization called AWaRe. These categories were developed taking into account the impact of different antibiotics and classes on antimicrobial resistance, and the implications for their appropriate use. The 2023 AWaRe classification provides empiric guidance on 41 essential antibiotics for over 30 clinical infections targeting both the primary health care and hospital facility setting. A further 257 antibiotics not included on the EML have been allocated an AWaRe group for stewardship and monitoring purposes. This article describes the development of AWaRe focussing on the clinical evidence base that guided the selection of Access, Watch or Reserve antibiotics as first and second choices for each infection. The overarching objective was to offer a tool for optimising the quality of global antibiotic prescribing and reduce inappropriate use by encouraging the use of Access antibiotics (or no antibiotics) where appropriate. This clinical evidence evaluation and subsequent EML recommendations are the basis for the AWaRe antibiotic book and related smartphone applications. By providing guidance on antibiotic prioritization, AWaRe aims to facilitate the revision of national lists of essential medicines, update of national prescribing guidelines and surveillance of antibiotic use. Adherence to AWaRe would extend the effectiveness of current antibiotics while helping countries to expand access to these life-saving medicines for the benefit of current and future patients, health professionals, and the environment.
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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".