Evaluation of Three Rapid Methods for Detection of Methicillin Resistance in<i>Staphylococcus aureus</i>
Bibliographic record
Abstract
The probe-based Velogene Rapid MRSA Identification Assay (ID Biomedical Corp., Vancouver, British Columbia, Canada) and the latex agglutination MRSA-Screen (Denka Seiken Co., Tokyo, Japan) were evaluated for their ability to identify methicillin-resistant Staphylococcus aureus (MRSA) and to distinguish strains of MRSA from borderline oxacillin-resistant S. aureus (BORSA; mecA-negative, oxacillin MICs of 2 to 8 microgram/ml). The Velogene is a 90-min assay using a chimeric probe to detect the mecA gene. MRSA-Screen is a 15-min latex agglutination test with penicillin-binding protein 2a antibody-sensitized latex particles. We compared these assays with the BBL Crystal MRSA ID System (Becton Dickinson, Cockeysville, Md.) and with PCR for mecA gene detection. A total of 397 clinical isolates of S. aureus were tested, consisting of 164 methicillin-susceptible strains, 197 MRSA strains, and 37 BORSA strains. All assays performed well for the identification of MRSA with sensitivities and specificities for Velogene, MRSA-Screen, and BBL Crystal MRSA ID of 98.5 and 100%, 98.5 and 100%, and 98.5 and 98%, respectively. Three MRSA strains were not correctly identified by each of the Velogene and MRSA-Screen assays, but repeat testing with a larger inoculum resolved the discrepancies. The BBL Crystal MRSA ID test misclassified four BORSA strains as MRSA. Both the Velogene and the MRSA-Screen assays are easy to perform, can accurately differentiate BORSA isolates from MRSA isolates, and provide a rapid alternative for the detection of methicillin resistance in S. aureus in clinical laboratories, especially when mecA PCR gene detection is unavailable.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".