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Record W1148383869 · doi:10.1155/2009/518471

Antimicrobial Susceptibility of 6685 Organisms Isolated from Canadian Hospitals: CANWARD 2007

2009· article· en· W1148383869 on OpenAlexafffundabout
George G. Zhanel, Mel DeCorby, Kim Nichol, Aleksandra Wierzbowski, Patricia J. Baudry, Franil Tailor, Philippe Lagacé‐Wiens, Andrew Walkty, Sergio Fanella, Oscar Larios, Michael R. Mulvey, Melissa McCracken, James A. Karlowsky

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicBacterial Identification and Susceptibility Testing
Canadian institutionsHealth CanadaUniversity of ManitobaHealth Sciences Centre
FundersAstellas PharmaUniversity of AlbertaHealth CanadaBayerUniversity of ManitobaWyethPfizer CanadaMerck
KeywordsTigecyclineBroth microdilutionLinezolidMicrobiologyCefepimeAntimicrobialDalbavancinDaptomycinMeropenemMedicineVancomycinPiperacillinErtapenemTazobactamMinimum inhibitory concentrationMethicillin-resistant Staphylococcus aureusAmikacinStaphylococcus aureusAntibiotic resistanceAntibioticsImipenemBiologyBacteria

Abstract

fetched live from OpenAlex

BACKGROUND: Antimicrobial resistance is a growing problem in North American hospitals as well as hospitals worldwide. OBJECTIVES: To assess the antimicrobial susceptibility patterns of commonly used agents against the 20 most common organisms isolated from Canadian hospitals. METHODS: In total, 7881 isolates were obtained between January 1, 2007, and December 31, 2007, from 12 hospitals across Canada as part of the Canadian Ward Surveillance Study (CANWARD 2007). Of these, 6685 isolates (20 most common organisms) obtained from bacteremic, urinary, respiratory and wound specimens underwent antimicrobial susceptibility testing. Susceptibility testing was assessed using the Clinical and Laboratory Standards Institute broth microdilution method. RESULTS: The most active (based upon minimum inhibitory concentration [MIC] data only) agents against methicillin‐resistant Staphylococcus aureus (MRSA) and methicillin‐resistant Staphylococcus epidermidis (MRSE) were dalbavancin, daptomycin, linezolid, telavancin, tigecycline and vancomycin, with MICs required to inhibit the growth of 90% of organisms (MIC90) of 0.06 μg/mL and 0.06 μg/mL, 0.25 μg/mL and 0.25 μg/mL, 4 μg/mL and 1 μg/mL, 0.25 μg/mL and 0.25 μg/mL, 0.5 μg/mL and 0.25 μg/mL, and 1 μg/mL and 2 μg/mL, respectively. The most active agents against vancomycin‐resistant enterococci were daptomycin, linezolid and tigecycline with MIC of 2 μg/mL, 4 μg/mL and 0.12 μg/mL, respectively. The most active agents against Escherichia coli were amikacin, cefepime, ertapenem, meropenem, piperacillin‐tazobactam and tigecycline with MIC of 4 μg/mL, 2 μg/mL, 0.06 μg/mL or less, 0.12 μg/mL or less, 4 μg/mL and 1 μg/mL, respectively. The most active agents against extendedspectrum beta‐lactamase‐producing E coli were ertapenem, meropenem and tigecycline with MIC of 0.12 μg/mL or less, 0.12 μg/mL or less and 1 μg/mL, respectively. The most active agents against Pseudomonas aeruginosa were amikacin, cefepime, meropenem and piperacillin‐tazobactam with MIC of 32 μg/mL, 32 μg/mL, 8 μg/mL and 64 μg/mL, respectively. The most active agents against Stenotrophomonas maltophilia were tigecycline and trimethoprimsulfamethoxazole and levofloxacin with MIC of 8 μg/mL, 8 μg/mL and 8 μg/mL, respectively. The most active agents against Acinetobacter baumannii were amikacin, fluoroquinolones (eg, levofloxacin), meropenem, and tigecycline with MIC of 2 μg/mL or less, 1 μg/mL, 4 μg/mL and 2 μg/mL, respectively. CONCLUSIONS: The most active agents versus Gram‐positive cocci from Canadian hospitals were vancomycin, linezolid, daptomycin, tigecycline, dalbavancin and telavancin. The most active agents versus Gram‐negative bacilli from Canadian hospitals were amikacin, cefepime, ertapenem (not P aeruginosa ), meropenem, piperacillintazobactam and tigecycline (not P aeruginosa ). Colistin (polymyxin E) was very active against P aeruginosa and A baumannii .

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.660
Threshold uncertainty score0.873

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.004
GPT teacher head0.214
Teacher spread0.210 · 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 designObservational
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".

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Citations3
Published2009
Admission routes3
Has abstractyes

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