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Record W2133210748 · doi:10.5539/mas.v3n5p51

Performance of CHROM Agar and Oxacillin Resistant Screening Agar Base Media for Detection of Methicillin Resistant Staphylococcus aureus (MRSA) from Chronic Wound

2009· article· en· W2133210748 on OpenAlexvenueno aff
E.S. Karthy, P. Ranjitha, A. Mohankumar

Bibliographic record

VenueModern Applied Science · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsnot available
Fundersnot available
KeywordsAgarStaphylococcus aureusMicrobiologyMethicillin-resistant Staphylococcus aureusCiprofloxacinAgar plateMedicineBiologyAntibioticsBacteria

Abstract

fetched live from OpenAlex

CHROM agar Staphylococcus aureus and Oxacillin Resistant Screening Agar Base (ORSAB) media with oxacillin were evaluated for the screening of Methicillin Resistant Saphylococcus aureus (MRSA). Among 190 samples, totally 126 confirmed Staphylococcus aureus strains were used for screening of MRSA used CHROM agar and ORSAB media were compared with the other MRSA screening media like Baird Park agar (BPA) with ciprofloxacin, Mannitol salt agar (MSA) with oxacillin, Blood agar (BA) with oxacillin and Muller Hinton agar (MHA) with oxacillin. Totally 54 MRSA strains were confirmed using PCR, among that 83% and 92% of the MRSA stains were isolated as mauve colonies on CHROM agar and blue colonies on ORSAB medium in 24 hrs incubation, compare with 64%, 61%, 50%, and 42% of the strains that were isolated on BPA, MSA, BA and MHA respectively. After 48 hrs of incubation 100%, 98%, 77%, 77%, 72% and 68% of the MRSA strains were isolated on CHROM agar, ORSAB, BPA, MSA, BA and MHA respectively. CHROM agar Staphylococcus aureus and ORSAB agar proved to be more sensitivity and specificity than other MRSA selective media. These provide an alternative for the detection of MRSA in clinical laboratories, especially when PCR is unavailable.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.550

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.252
Teacher spread0.230 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2009
Admission routes1
Has abstractyes

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