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Record W1937301787 · doi:10.1155/2009/725872

Risk Factors for and Outcomes Associated with Clinical Isolates of <i>Escherichia coli</i> and <i>Klebsiella</i> Species Resistant to Extended‐Spectrum Cephalosporins among Patients Admitted to Canadian Hospitals

2009· article· en· W1937301787 on OpenAlexafffundabout
Marianna Ofner-Agostini, Andrew E. Simor, Michael R. Mulvey, Allison McGeer, Zahir Hirji, Melissa McCracken, Denise Gravel, David A. Boyd, Elizabeth Bryce

Bibliographic record

VenueCanadian Journal of Infectious Diseases and Medical Microbiology · 2009
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAntibiotic Resistance in Bacteria
Canadian institutionsVancouver General HospitalMount Sinai HospitalHealth Sciences CentrePublic Health OntarioUniversity Health NetworkUniversity of TorontoSunnybrook Health Science CentrePublic Health Agency of Canada
FundersHospital for Sick ChildrenCentre Hospitalier Universitaire de QuébecUniversity of AlbertaJewish General HospitalPublic Health AgencyPublic Health Agency of CanadaLondon Health Sciences CentreHamilton Health Sciences
KeywordsCephalosporinLogistic regressionInternal medicineMultivariate analysisMedicineUnivariate analysisAntibioticsDemographicsUnivariateMultivariate statisticsBiologyMicrobiologyDemography

Abstract

fetched live from OpenAlex

BACKGROUND: Clinical features associated with Gram-negative bacterial isolates with extended-spectrum beta-lactamase (ESBL)- and AmpC-mediated resistance identified in Canadian hospitals is largely unknown. The objective of the present study was to determine the demographics, risk factors and outcomes of patients with ESBL- or AmpC-mediated resistant organisms in Canadian hospitals. METHODS: Patients with clinical cultures of Escherichia coli or Klebsiella species were matched with patients with a similar organism but susceptible to third-generation cephalosporins. Molecular identification of the AmpC or ESBL was determined using a combination of polymerase chain reaction and sequence analysis. Univariate and multivariate logistic regression analysis was performed to identify variables associated with becoming a case. RESULTS: Eight Canadian hospitals identified 106 cases (ESBL/AmpC) and 106 controls. All risk factors identified in the univariate analysis as a predictor of being an ESBL/AmpC cases at the 0.20 P-value were included in the multivariate analysis. No significant differences in outcomes were observed (unfavourable responses 17% versus 15% and mortality rates 13% versus 7%, P not significant). Multivariate logistic regression found an association of becoming an ESBL/AmpC case with: previous admission to a nursing home (OR 8.28, P=0.01) or acute care facility (OR 1.96, P=0.03), length of stay before infection (OR 3.05, P=0.004), and previous use of first-generation cephalosporins (OR 2.38, P=0.02) or third-generation cephalosporins (OR 4.52, P=0.01). Appropriate antibiotics were more likely to be given to controls (27.0% versus 13.3%, P=0.05) and number of days to appropriate antibiotics was longer for cases (median 2.8 days versus 1.2 days, P=0.05). CONCLUSION: The importance of patient medical history, present admission and antibiotic use should be considered for all E coli or Klebsiella species patients pending susceptibility testing results.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.593
Threshold uncertainty score0.809

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.223
Teacher spread0.219 · 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 source (direct Gemma or distilled Codex), 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".

Quick stats

Citations20
Published2009
Admission routes3
Has abstractyes

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Same venueCanadian Journal of Infectious Diseases and Medical MicrobiologySame topicAntibiotic Resistance in BacteriaFrench-language works237,207