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Record W4400720930 · doi:10.1016/s2214-109x(24)00229-8

A successful UN High-Level Meeting on antimicrobial resistance must build on the 2023 UN High-Level Meeting on tuberculosis

2024· article· en· W4400720930 on OpenAlexafffund
Daniela María Cirillo, Richard Anthony, Sébastien Gagneux, C. Robert Horsburgh, Rumina Hasan, Saffiatou Darboe, Rafael Laniado-Laborin, Ari Probandari, Nestani Tukvadze, Ricardo Alexandre Arcêncio, John Bimba, Susanna Brighenti, Dumitru Chesov, Chen‐Yuan Chiang, Gulmira Kalmambetova, Gwenan M. Knight, Olha Konstantynovska, Alexandra Kruse, Christoph Lange, Harriet Mayanja‐Kizza, Emma S. McBryde, Jinsoo Min, Adrián Sánchez‐Montalvá, Giorgia Sulis, Bazarragchaa Tsogt, Martie van der Walt, Dorothy Yeboah‐Manu, Janika Hauser

Bibliographic record

VenueThe Lancet Global Health · 2024
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsUniversity of Ottawa
FundersFogarty International CenterCollege of Medicine, Catholic University of KoreaNoguchi Memorial Institute for Medical Research, University of GhanaJesus College, University of CambridgeFundació Institut de Recerca Hospital Universitari Vall d’HebronUniversitas Sebelas MaretInternational Union Against Tuberculosis and Lung DiseaseUniversidade de São PauloUniversity of GhanaUniversität zu LübeckCatholic University of KoreaMedical Research CouncilUniversidad Autónoma de Baja CaliforniaLondon School of Hygiene and Tropical MedicineGentofte HospitalJames Cook UniversityKarolinska InstitutetUniversity of KarachiRijksinstituut voor Volksgezondheid en MilieuSouth African Medical Research CouncilUniversity of Ottawa
KeywordsTuberculosisHigh resistanceMedicineMycobacterium tuberculosisAntibiotic resistanceEnvironmental healthMicrobiologyBiologyAntibioticsPathology

Abstract

fetched live from OpenAlex

In September, world leaders will meet at the UN High-Level Meeting (UNHLM) on Antimicrobial Resistance (AMR).1 Drug-resistant tuberculosis is one of the major drivers of AMR-associated morbidity and mortality globally.2 The emergence and spread of AMR has set the tuberculosis response back decades. Advancement in research and political leadership, including through two UNHLMs on tuberculosis, have developed new pathways for progress in addressing tuberculosis.3 These efforts will be fatally undermined if governments fail to increase efforts to address AMR.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.321
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.028
GPT teacher head0.289
Teacher spread0.261 · 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 designNot applicable
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

Citations7
Published2024
Admission routes2
Has abstractyes

Explore more

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