WHO's essential medicines and AWaRe: recommendations on first- and second-choice antibiotics for empiric treatment of clinical infections
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
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 have been 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 empirical 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 optimizing 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 national prescribing guidelines, and supervise antibiotic use. Adherence to AWaRe would extend the effectiveness of current antibiotics while helping countries expand access to these life-saving medicines for the benefit of current and future patients, health professionals, and the environment.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 it