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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".