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Record W2922853445 · doi:10.1128/aac.00126-19

Pharmacodynamic Attainment of the Synergism of Meropenem and Fosfomycin Combination against Pseudomonas aeruginosa Producing Metallo-β-Lactamase

2019· article· en· W2922853445 on OpenAlexaff
James Albiero, Josmar Mazucheli, Juliana Pimenta dos Reis Barros, Márcia Maria dos Anjos, Sheila Alexandra Belini Nishiyama, Floristher Elaine Carrara-Marroni, Serubbabel Sy, Matthew Fidler, Sherwin K. B. Sy, Maria Cristina Bronharo Tognim

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsUniversity of Waterloo
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsFosfomycinMeropenemPseudomonas aeruginosaMicrobiologyAntimicrobialPharmacodynamicsAntibioticsMultiple drug resistancePharmacologyMedicineBiologyBacteriaAntibiotic resistancePharmacokinetics

Abstract

fetched live from OpenAlex

Fosfomycin combined with other antimicrobials has shown good efficacy against multidrug-resistant (MDR) bacteria in both in vitro and clinical studies; however, the activity of fosfomycin combined with other antimicrobials against metallo-β-lactamase (MBL)-producing Pseudomonas aeruginosa strains has not been tested. The objective of this study was to determine the synergism and optimal intravenous dosing regimens of fosfomycin with meropenem against MDR and MBL-producing P. aeruginosa strains.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.225
Teacher spread0.220 · 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 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

Citations40
Published2019
Admission routes1
Has abstractyes

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