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Record W2300680829 · doi:10.1128/aac.02240-15

Effect of High-Dose Antimicrobials on Biofilm Growth of Achromobacter Species Isolated from Cystic Fibrosis Patients

2015· article· en· W2300680829 on OpenAlexaff
Sean Kevin Tom, Yvonne Yau, Trevor Beaudoin, John J. LiPuma, Valerie Waters

Bibliographic record

VenueAntimicrobial Agents and Chemotherapy · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInfections and bacterial resistance
Canadian institutionsSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsTobramycinColistinMicrobiologyAztreonamAmikacinLevofloxacinCystic fibrosisBiofilmAchromobacterAntibioticsBiologyAntimicrobialAminoglycosideBacteriaGentamicinPseudomonasAntibiotic resistanceImipenem

Abstract

fetched live from OpenAlex

MICs and biofilm inhibitory concentrations (BICs) were measured for 68 cystic fibrosis (CF) Achromobacter isolates for amikacin, aztreonam, colistin, levofloxacin, and tobramycin. With the exception of colistin and levofloxacin, the remaining antibiotics had MIC90s, BICs at which 50% of the isolates were susceptible (BIC50s), and BICs at which 90% of the isolates were susceptible (BIC90s) equal to or above the highest concentrations tested. In a biofilm model, tobramycin was able to significantly increase killing of bacterial cells compared to controls, for intermediate-resistant strains only, at concentrations of 1,000 and 2,000 μg/ml.

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 score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.006
GPT teacher head0.220
Teacher spread0.214 · 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

Citations22
Published2015
Admission routes1
Has abstractyes

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