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Record W4392968148 · doi:10.1016/j.vaccine.2024.02.072

Vaccine value profile for Klebsiella pneumoniae

2024· article· en· W4392968148 on OpenAlexaff
Ziyaad Dangor, Nicole M. Benson, James A. Berkley, Julia Bielicki, Merijn W. Bijsma, Jonathan Broad, Ed T. Buurman, Alan S. Cross, Erin M Duffy, Kathryn E. Holt, Pui‐Ying Iroh Tam, Mark Jit, Konstantinos Karampatsas, Michael Katwere, Gaurav Kwatra, Ramanan Laxminarayan, Kirsty Le Doaré, Robert Mboizi, Francesca Micoli, Catrin E. Moore, Eve Nakabembe, Nichola R. Naylor, Seamus O’Brien, Courtney P. Olwagen, Denasha L. Reddy, Charlene M.C. Rodrigues, David A. Rosen, Manish Sadarangani, Padmini Srikantiah, Sharon M. Tennant, Mateusz Hasso-Agopsowicz, Shabir A. Madhi

Bibliographic record

VenueVaccine · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsBC Children's HospitalUniversity of British Columbia
FundersWorld Health OrganizationMedical Research CouncilBill and Melinda Gates Foundation
KeywordsKlebsiella pneumoniaeMedicineVaccinationAntibiotic resistanceImmunologyImmunosuppressionIntensive care medicineAntibioticsMicrobiologyBiology

Abstract

fetched live from OpenAlex

Klebsiella pneumoniae causes community- and healthcare-associated infections in children and adults. Globally in 2019, an estimated 1.27 million (95% Uncertainty Interval [UI]: 0.91-1.71) and 4.95 million (95% UI: 3.62-6.57) deaths were attributed to and associated with bacterial antimicrobial resistance (AMR), respectively. K. pneumoniae was the second leading pathogen in deaths attributed to AMR resistant bacteria. Furthermore, the rise of antimicrobial resistance in both community- and hospital-acquired infections is a concern for neonates and infants who are at high risk for invasive bacterial disease. There is a limited antibiotic pipeline for new antibiotics to treat multidrug resistant infections, and vaccines targeted against K. pneumoniae are considered to be of priority by the World Health Organization. Vaccination of pregnant women against K. pneumoniae could reduce the risk of invasive K.pneumoniae disease in their young offspring. In addition, vulnerable children, adolescents and adult populations at risk of K. pneumoniae disease with underlying diseases such as immunosuppression from underlying hematologic malignancy, chemotherapy, patients undergoing abdominal and/or urinary surgical procedures, or prolonged intensive care management are also potential target groups for a K. pneumoniae vaccine. A 'Vaccine Value Profile' (VVP) for K.pneumoniae, which contemplates vaccination of pregnant women to protect their babies from birth through to at least three months of age and other high-risk populations, provides a high-level, holistic assessment of the available information to inform the potential public health, economic and societal value of a pipeline of K. pneumoniae vaccines and other preventatives and therapeutics. This VVP was developed by a working group of subject matter experts from academia, non-profit organizations, public-private partnerships, and multi-lateral organizations, and in collaboration with stakeholders from the WHO. All contributors have extensive expertise on various elements of the K.pneumoniae VVP and collectively aimed to identify current research and knowledge gaps. The VVP was developed using only existing and publicly available information.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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 head0.007
GPT teacher head0.262
Teacher spread0.255 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations62
Published2024
Admission routes1
Has abstractyes

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