MétaCan
Menu
Back to cohort
Record W2889184420 · doi:10.14745/ccdr.v40is2a02

Antimicrobial resistance surveillance in Canadian hospitals, 2007−2012

2014· article· en· W2889184420 on OpenAlexafffundvenueabout
Denise Gravel, CP Archibald, Linda Pelude, M. R. Mulvey, George R. Golding

Bibliographic record

VenueCanada Communicable Disease Report · 2014
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntibiotic Use and Resistance
Canadian institutionsPublic Health Agency of Canada
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsMedicineClostridium difficileInfection controlAntibiotic resistanceMethicillin-resistant Staphylococcus aureusAntibioticsStaphylococcus aureusIntensive care medicineMicrobiologyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: The Canadian Nosocomial Infection Surveillance Program (CNISP) is a collaborative effort of the Public Health Agency of Canada's Centre for Communicable Diseases and Infection Control, the National Microbiology Laboratory, and 54 largely university-affiliated tertiary care sentinel hospitals in 10 provinces across Canada. OBJECTIVE: To provide a summary of antibiotic resistance rates of four key antibiotic resistant organisms in major hospitals across Canada from January 1, 2007, to December 31, 2012. METHODS: Patients' clinical and demographic data and associated results of laboratory analyses were submitted to the Agency by participating hospitals. The infection rates were summarized per 1,000 patient admissions at national and regional levels. RESULTS: (CRE) have been measured since 2010 and have been low and stable, with 0.11 CRE infections per 1,000 patient admissions in 2010 and 0.14 CRE infections per 1,000 admissions in 2012, with higher rates in the Western and Central regions and lower rates in the Eastern region. CONCLUSION: In Canada, of the four antibiotic resistant organisms under surveillance, HA-CDI and MRSA have been gradually decreasing, VRE is low but rising, and CRE remains low with Western and Central rates consistently higher than Eastern rates.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.275
Threshold uncertainty score0.960

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 science0.0010.000
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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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 designNot applicable
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
Published2014
Admission routes4
Has abstractyes

Explore more

Same venueCanada Communicable Disease ReportSame topicAntibiotic Use and ResistanceFrench-language works237,207