SynthesisFrontiers in public health2026
Machine learning models in predicting antimicrobial resistance in gonorrhea: a systematic review and meta-analysis.
Synthesis in Frontiers in public health, 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.
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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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Authors and funding
3 authors.
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Abstract
Background: The global rise in antimicrobial resistance (AMR) among Aim: This systematic review and meta-analysis aimed to evaluate the diagnostic accuracy of machine learning models in predicting antimicrobial resistance in Methods: A comprehensive search of seven databases PubMed, Scopus, Web of Science, Embase, CINAHL, IEEE Xplore, and Google Scholar was conducted for studies published up to 2025. Eligible studies applied ML algorithms to genomic, phenotypic, or epidemiological datasets for predicting AMR in Results: Five eligible studies encompassing unique Conclusion: Machine learning models exhibit outstanding diagnostic accuracy in predicting AMR in
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