Validation of a Gradient Diffusion Method (Etest) for Testing of Antimicrobial Susceptibility of Aerococcus urinae to Fluoroquinolones
Bibliographic record
Abstract
Aerococcus urinae is a urinary pathogen with well-described resistance to fluoroquinolones. This study aimed to validate the gradient diffusion (GD) method (Etest) on cation-adjusted Mueller-Hinton agar with 5% sheep blood for testing the susceptibilities of Aerococcus urinae to the antimicrobial agents ciprofloxacin and levofloxacin and to compare the Etest to the broth microdilution (BMD) method from CLSI document M45-A3. Agar dilution (AD), as recommended by EUCAST, was used as an alternative reference method to arbitrate discrepancies or address technical issues. Aerococcus urinae isolates from urinary specimens were prospectively collected between June 2016 and December 2017 from six hospitals in Quebec, Canada, and identifications were confirmed using Vitek MS with the IVD 3.0 database. Of the 207 isolates tested using BMD, 37 (17.9%) showed trailing and 19 (9.2%) showed insufficient growth; these were tested using AD. Also, 38 isolates (18.4%) for ciprofloxacin and 13 isolates (6.3%) for levofloxacin showed a lack of essential or categorical agreement between the Etest and BMD and were also tested by AD. By use of a combined reference method (BMD or AD), the susceptibility rates of Aerococcus urinae were 82.6% and 81.6% for ciprofloxacin and levofloxacin, respectively. Categorical agreement between GD and the combined reference methods was 95.2% for ciprofloxacin and 97.1% for levofloxacin, with no very major error identified. Major and minor error rates were 0.6% and 4.3% for ciprofloxacin and 1.2% and 1.9% for levofloxacin. Overall, antimicrobial susceptibility testing (AST) using the Etest on sheep blood agar showed good agreement with the reference methods and can be considered by clinical laboratories wishing to perform AST on Aerococcus urinae isolates.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".