ArticleFrontiers in pharmacology2025
The WHO's critical bacteria list: scientific response eight years after its implementation and development of an AI-based tool for its monitoring.
Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- The WHO priority list of antibiotic-resistant bacteria: challenges and opportunities for next-generation antimicrobial development.Frontiers in pharmacology · 2026Review
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: In 2017, the World Health Organization (WHO) issued a global alert identifying 12 bacteria in urgent need of new treatments. Main body: This study assesses the scientific community's response to this alert by analyzing original research publications using LLMzCor, an AI-based tool developed and validated by our group. To compare trends, we focused on publications from 5 years before and after the alert, specifically on three bacteria listed in the WHO alert, sorted by priority level: Results: Although overall publications increased after the WHO alert, no statistically significant changes were found in the reports of Resistant strains over time. The development of New treatments for the listed bacteria showed a slight increase, between 2% and 10%. Furthermore, Immunization strategies remained relatively unchanged, with less than 2%. Meanwhile, LLMzCor demonstrated robust performance across categories, F1-scores ranging from 0.65 to 0.72 in key classifications, while recall peaked at 0.75, indicating a high capacity to identify relevant articles. These results support the model's reliability for large-scale automated classification of scientific abstracts. Conclusion: These findings, supported by LLMzCor, underscore the urgency of a stronger WHO alert and action plans to develop new strategies against bacterial resistance.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.