Evidence map›Paper›PMID 41020009›Full record

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.

Juan Eduardo Robledo Almonacid, Christian Lombardo, Mariana Romano, Agustina Quiroga, Paula Cambuli Bianchi, Mauricio Hualpa, Constanza Giai, Xiomara María A Oviedo, Ramiro Alejo Salgado Mansur, Mariana Guadalupe Vallejo and 1 more

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Juan Eduardo Robledo AlmonacidFarmacognosia, Departamento de Ciencias Farmacéuticas, Facultad de Ciencia Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina.
Christian LombardoFacultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires, Buenos Aires, Argentina.
Mariana RomanoLaboratorio de Biología Celular y Molecular-BioCyM. Universidad Juan Agustín Maza, Mendoza, Argentina.
Agustina QuirogaLaboratorio de Biología Celular y Molecular-BioCyM. Universidad Juan Agustín Maza, Mendoza, Argentina.
Paula Cambuli BianchiInstituto de Investigaciones Biomédicas-INBIOMED-UM Facultad de Ciencias Médicas, Universidad de Mendoza, Mendoza, Argentina.
Mauricio HualpaObra Social de Empleados Públicos-OSEP-Mendoza, Mendoza, Argentina.
Constanza GiaiLaboratorio de Biología Celular y Molecular-BioCyM. Universidad Juan Agustín Maza, Mendoza, Argentina.
Xiomara María A OviedoDepartamento Académico de Ciencias Exactas, Físicas y Naturales, Universidad Nacional de La Rioja, La Rioja, Argentina.
Ramiro Alejo Salgado MansurFarmacognosia, Departamento de Ciencias Farmacéuticas, Facultad de Ciencia Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina.
Mariana Guadalupe Vallejo *Farmacognosia, Departamento de Ciencias Farmacéuticas, Facultad de Ciencia Químicas, Universidad Nacional de Córdoba, Córdoba, Argentina.
Cristián Andrés Quintero *Laboratorio de Biología Celular y Molecular-BioCyM. Universidad Juan Agustín Maza, Mendoza, Argentina.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

antibioticantibiotic resistanceautomatic classifiertreatmentWHO

Identifiers

PMID41020009
PMCPMC12461225

What OpenQuestion holds

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Registered trials

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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.