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Record W4416971374 · doi:10.1093/jacamr/dlaf230.006

O06 Development of AWaRe antibiotic quality indicators for optimal use

2025· article· en· W4416971374 on OpenAlexaff
Annie Heath, Jan Goelen, Pem Chuki, Aislinn Cook, Filip Djukic, Nga Thi Thuy, Elisa Funiciello, Sumanth Gaundra, Brian Godman, Yara Khalaf, Guilia Lorenzetti, Marc Mendelson, Catrin E. Moore, Cláudia Garcia Serpa Osorio-de-Castro, Zikria Saleem, Jeroen Schouten, Elizabeth Tayler, Evelyn Wesangula, A W aR e-B ased Q I s Delphi Panel, Stephen Campbell

Bibliographic record

VenueJAC-Antimicrobial Resistance · 2025
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsDocumentationDelphi methodCLARITYQuality (philosophy)Primary careMedical recordPerformance indicatorAntibiotic resistance

Abstract

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Abstract Background The use of antibiotics varies by setting—hospital, outpatient/primary care—with a disproportionate impact of antimicrobial resistance in low-and middle-income countries. The WHO AWaRe (Access/Watch/Reserve) book gives detailed guidance on the optimal use of antibiotics across primary care and hospitals for adults and children with the aim of improving the quality of use of essential antibiotics. Objectives To develop universally applicable, model sets of appropriate and feasible quality indicators based on the WHO AWaRe system for primary care, hospital and general indicators for optimal antibiotic use. Methods Indicators identified in a scoping review were revised to focus on primary care and hospital facility infections in the AWaRe book. Condition-specific indicators captured measures such as appropriate antibiotic use, total daily dose and proportion of Access or Watch antibiotics. General indicators were developed from prescribing and dispensing guidelines in the AWaRe book and covered themes from proper documentation in medical records to population-level antibiotic use. The indicators were evaluated through a two-round Global Delphi Technique of 104 and 107 panellists, respectively, to determine appropriateness and feasibility in national and local settings, followed by a two-round RAND/UCLA Appropriateness Method (RAND/UCLA) with 12 panellists rating indicators on a global scale. Panels comprised experts from every WHO region. In Round 1 of each method, panellists also rated clarity and could suggest rewording or new indicators. Findings from Round 2 are reported. The median rating was calculated for each indicator to determine agreement. A median of 7–9 was considered appropriate or feasible. If ≥80% of panellists rated within ±1 of the 7–9 median, the indicator was considered appropriate with agreement. Results The indicators covered nine clinical conditions in primary care and nine in hospital settings, including respiratory tract infections, diarrhoea, urinary tract infections (UTIs), sepsis and others. There were 102 indicators (Primary Care: 46; Hospital: 39; General: 17) in Round 2 of the Delphi Technique. Of these, 100% were rated appropriate and 99 (97.1%) feasible in a local context. During the RAND/UCLA method, 136 indicators (Primary Care: 56; Hospital: 60; General: 20) were rated in Round 2, with 131 (96.3%) appropriate and 72 (52.9%) feasible in a global context. From these broad sets, 12 indicators from the Delphi Technique and 31 indicators from the RAND/UCLA method were rated both appropriate and feasible with agreement respectively. Most indicators rated appropriate and feasible with agreement by the Delphi panel measured the proportion of patients receiving Access or Watch antibiotics across clinical conditions. Those rated appropriate and feasible with agreement by the RAND/UCLA panel measured appropriate antibiotic choice, dose, duration and the proportion of Access and Watch antibiotics Conclusions These model AWaRe-based, universally applicable quality indicators can be locally adapted and measured with different tools to improve the optimal use of antibiotics and inform global and country specific antimicrobial stewardship programmes (AMS).

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.112
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.293
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2025
Admission routes1
Has abstractyes

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