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Record W2216312537 · doi:10.1017/s0022029915000242

Comparison of phenotypic tests for detecting penicillin G resistance with presence of<i>blaZ</i>gene in<i>Staphylococcus aureus</i>isolated from bovine intramammary infections

2015· article· en· W2216312537 on OpenAlexaff
Norma Russi, Julia Maito, Silvina A Dieser, María Sol Renna, Marcelo Signorini, Cecilia Camussone, Verónica E. Neder, Martín Pol, Tirante Liliana, Liliana Odierno, Luis F. Calvinho

Bibliographic record

VenueJournal of Dairy Research · 2015
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicMilk Quality and Mastitis in Dairy Cows
Canadian institutionsCytodiagnostics (Canada)
Fundersnot available
KeywordsStaphylococcus aureusPenicillinGenotypeMastitisMicrobiologyPhenotypeGeneBiologyAntibioticsBacteriaGenetics

Abstract

fetched live from OpenAlex

Few studies have described the relationship between genotypic and phenotypic methods for detecting penicillin resistance in Staphylococcus aureus isolated from bovine intramammary infection (IMI). Six phenotypic methods for penicillinase detection were compared with a genotypic method testing the presence of the β-lactamase gene blaZ in Staph. aureus (n = 150) isolated from bovine IMI. Highest sensitivities and specificities were observed for disk diffusion (DD) (93 and 97.4%), minimum inhibitory concentration (MIC) (90.3 and 97.4%), Cefinase™ (85.9 and 97.4%) and Diatabs™ (85.7 and 98.7%). The estimated cut-off points estimated in the present study can be considered close to the ones indicated by CLSI (2013). The molecular detection of blaZ gene is the only method that may indicate the real or potential capacity of producing β-lactamase in Staph. aureus. Considering that from a clinical standpoint a false negative result from a phenotypic test is the most unfavourable situation, a combination of standard DD with Diatabs™ or Cefinase™ should be performed by routine mastitis laboratories to minimise false negative results.

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)0.003
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.131
GPT teacher head0.372
Teacher spread0.241 · 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

Citations16
Published2015
Admission routes1
Has abstractyes

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