ReviewJAC-antimicrobial resistance2026
Use of standard definitions for the extent of antibiotic resistance in bacteria: a systematic review and framework for future consensus definitions.
Review in JAC-antimicrobial resistance, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Background: Various terms are used to categorize the extent of antibiotic resistance in bacteria. It is important to standardize the classification of drug-resistant bacteria to ensure clarity and generalizability of evidence across different disciplines and inform policy. Methods: A literature search for articles published between 2011 and August 2025 indexed in MEDLINE and EMBASE, which used the terms: multidrug resistance, extensively drug resistance, pan-drug resistance, difficult-to-treat or usual drug resistance in either the methods or results sections, and which reported bacterial pathogens relevant to human health was performed. Data extracted were analysed quantitatively using multivariable logistic regressions and qualitatively to examine the study characteristics that adopted various definitions. Results: From 2465 eligible records, the most commonly used definition was by Magiorakos Conclusion: The progressive development of antibiotic resistance necessitates updated definitions to include newer antibiotic agents and genotypes. These revised definitions can also prioritize antibiotic classes according to their efficacy profile, toxicity and availability, and should be validated against clinical datasets to correlate with patient outcomes.
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