Rapid Detection of <i>mecA</i> -Positive and <i>mecA</i> -Negative Coagulase-Negative Staphylococci by an Anti-Penicillin Binding Protein 2a Slide Latex Agglutination Test
Bibliographic record
Abstract
A rapid slide latex agglutination (LA) test, MRSA-Screen (Denka Seiken Co., Niigata, Japan), which detects PBP 2a, was tested for its ability to differentiate between mecA-positive and -negative coagulase-negative staphylococci. A total of 463 isolates from 13 species were included in the study. The mecA gene was detected by PCR, and the oxacillin MIC was determined by the agar dilution method according to the guidelines of the National Committee for Clinical Laboratory Standards (NCCLS). The LA test was performed with oxacillin-induced isolates. The true-positive and true-negative results were defined on the basis of the presence or the absence of the mecA gene. By PCR, 251 isolates were mecA positive and 212 were mecA negative. The sensitivities, specificities, and positive and negative predictive values for the LA test compared to the NCCLS breakpoint for oxacillin resistance (>/=0.5 mg/liter) were as follows: for the LA test, 100, 99.5, 99.6, and 100%, respectively; for the NCCLS breakpoint, 100, 60.8, 75.1, and 100%, respectively. One hundred twenty-five mecA-positive isolates were also tested by the LA test without induction of PBP 2a; only 72 (57.6%) gave a positive result and required 3 to 15 min for reaction. With induction, all 251 isolates were positive within 3 min. The LA test was reliable in classifying mecA-negative isolates, but it classified isolates for which the oxacillin MIC was >/=0.5 mg/liter as oxacillin susceptible. For the reliable detection of oxacillin resistance by the MRSA-Screen in coagulase-negative staphylococci, induction of the mecA gene appears to be necessary.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".