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Record W2009492421 · doi:10.1159/000238842

Antimicrobial Activity of Subinhibitory Concentrations of Aminoglycosides against Pseudomonas aeruginosa as Determined by the Killing-Curve Method and the Postantibiotic Effect

2009· article· en· W2009492421 on OpenAlexaff
George G. Zhanel, James A. Karlowsky, D. Hoban, Ross Davidson

Bibliographic record

VenueChemotherapy · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicPharmaceutical and Antibiotic Environmental Impacts
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsTobramycinAmikacinAminoglycosideMinimum inhibitory concentrationGentamicinMicrobiologyAntimicrobialPseudomonas aeruginosaMinimum bactericidal concentrationAntibioticsChemistryBiologyBacteria

Abstract

fetched live from OpenAlex

This investigation used the postantibiotic effect (PAE) and killing curves to provide data on the antimicrobial activity of subinhibitory (1/8x, 1/4x and 1/2x minimum inhibitory concentration; MIC) and inhibitory (1x MIC) concentrations of amikacin, gentamicin and tobramycin against Pseudomonas aeruginosa. Subinhibitory concentrations (1/4x and 1/2x MIC) of aminoglycosides demonstrated a reproducible PAE. At 1/4x MIC, the order of duration of the PAE was approximately 15 min for all aminoglycosides, while at 1/2x MIC all three aminoglycosides displayed a similar PAE of approximately 40 min. Killing-curve studies demonstrated that subinhibitory concentrations of aminoglycosides either decrease bacterial growth for several hours (1/4x and 1/2x MIC) or produce stasis of growth for several hours (1/8x MIC). Only inhibitory aminoglycoside concentrations (1x MIC) proved to be bactericidal. Subinhibitory concentrations of aminoglycosides decrease bacterial growth and produce a PAE against P. aeruginosa.

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 score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.008
GPT teacher head0.271
Teacher spread0.263 · 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

Citations42
Published2009
Admission routes1
Has abstractyes

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