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Record W4308371391 · doi:10.1128/spectrum.03601-22

Evaluation of Dilution Susceptibility Testing Methods for Aztreonam in Combination with Avibactam against Enterobacterales

2022· article· en· W4308371391 on OpenAlexaff
C. J. Kelley, A. Kennedy-Mendez, O. N. Walser, M. T. Thwaites, Francis F. Arhin, Chris M. Pillar, David A. Hufnagel

Bibliographic record

VenueMicrobiology Spectrum · 2022
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsPfizer (Canada)
FundersBiomedical Advanced Research and Development AuthorityU.S. Department of Health and Human ServicesAdministration for Strategic Preparedness and Response
KeywordsBroth microdilutionAgar dilutionAgarAgar Dilution MethodMinimum inhibitory concentrationMicrobiologyIncubationBiologyFood scienceChemistryBacteriaAntimicrobialBiochemistry

Abstract

fetched live from OpenAlex

Increasing antibiotic resistance and emergence of pan-resistant isolates threaten the ability to control infections and to provide many other medical interventions such as surgery and chemotherapy, among others. New therapies are required to control emerging resistance mechanisms, including the increase in metallo-β-lactamases. Some new antibiotic combinations provide coverage against highly resistant isolates but are unable to target organisms that produce metallo-β-lactamases. Aztreonam in combination with avibactam provides a broad spectrum of activity against highly resistant isolates that also targets metallo-β-lactamase-producing organisms. An important part of drug development is the ability for clinical labs to determine the susceptibility of isolates to the antimicrobial. This manuscript investigates the in vitro susceptibility testing of aztreonam/avibactam with nonstandard testing conditions and a correlation study between broth microdilution and agar dilution against clinical isolates encoding a variety of resistance mechanisms. Overall, aztreonam/avibactam was generally unaffected by changes in testing conditions and showed strong agar/broth correlation.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.206
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.025
GPT teacher head0.316
Teacher spread0.290 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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