Evaluation of a New Medium, Oxacillin Resistance Screening Agar Base, for the Detection of Methicillin-Resistant <b> <i>Staphylococcus aureus</i> from Clinical Specimens </b>
Bibliographic record
Abstract
The incidence of infections caused by methicillin-resistantStaphylococcus aureus (MRSA) continues to increase in many countries worldwide. Rapid identification of MRSA from clinical specimens and screening of high-risk patients for MRSA colonization have been found to be cost-effective measures for limiting the spread of the organism in hospitals (1, 6). Several screening media for the enhanced recovery of methicillin-resistant organisms and for the differentiation of S. aureus from coagulase-negative staphylococci have been developed (2, 4, 5, 7, 8). Recently, a novel medium, oxacillin resistance screening agar base (ORSAB; Oxoid Limited, Basingstoke, England), has been developed for the detection of MRSA in clinical specimens. The medium uses aniline blue to demonstrate mannitol fermentation in staphylococci. The dual antibiotic supplement (oxacillin, 2.0 μg/ml; polymixin B, 50,000 IU/l) and the presence of 5.5% NaCl have the potential to reduce the growth of nonstaphylococcal organisms and to select for the growth of MRSA. We evaluated the performance of ORSAB as compared to that of mannitol salt agar supplemented with 2.0 μg/ml oxacillin (MSA) for the recovery of MRSA from clinical specimens.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".