Vancomycin MIC Susceptibility Testing of Methicillin-Susceptible and Methicillin-Resistant Staphylococcus aureus Isolates: A Comparison Between Etest® and an Automated Testing Method
Bibliographic record
Abstract
BACKGROUND: Vancomycin treatment failures and increased mortality have been reported in methicillin-resistant Staphylococcus aureus (MRSA) isolates with minimum inhibitory concentrations (MICs) >1 μg/mL. Most of this data utilized manual testing to determine the MIC. Recent vancomycin treatment guidelines do not specify the optimal testing method to define the MIC. METHODS: Over a twelve-month study period, we compared manual susceptibility testing by Etest® (AB Biodisk, Solna, Sweden) with automated testing by MicroScan Walk-Away® (Dade Behring, Inc., East Mississauga, Ontario) to determine the difference in the MICs among 383 sequential clinical S aureus isolates. RESULTS: Manual testing demonstrated MICs of 1.5 μg/mL or 2.0 μg/mL in 90% and 86% of MRSA and methicillin-sensitive Staphylococcus aureus (MSSA) isolates, respectively. Automated testing revealed MICs of 2.0 μg/mL for 56% and 54% of MRSA and MSSA isolates, respectively. The manual MIC test by Etest® was >1 μg/mL in 87% of MRSA isolates and 86% of methicillin-susceptible S aureus isolates in which the automated MIC result was 1 μg/mL. This same finding occurred in 94% (17/18) of S aureus isolates causing non-skin/skin structure infections. Among all subgroups of isolates, manual testing demonstrated statistically significant higher MICs compared to automated testing. CONCLUSIONS: MIC results generated by the Etest® consistently revealed a one dilution higher vancomycin MIC compared to MicroScan®. Automated MIC results of invasive MRSA isolates should be confirmed by manual Etest® to ensure identification of those isolates with vancomycin MICs >1μg/mL that are at risk for vancomycin treatment failure or increased mortality.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".